Personalized summary generation of data visualizations

ABSTRACT

Various embodiments are generally directed to systems for summarizing data visualizations (i.e., images of data visualizations), such as a graph image, for instance. Some embodiments are particularly directed to a personalized graph summarizer that analyzes a data visualization, or image, to detect pre-defined patterns within the data visualization, and produces a textual summary of the data visualization based on the pre-defined patterns detected within the data visualization. In various embodiments, the personalized graph summarizer may include features to adapt to the preferences of a user for generating an automated, personalized computer-generated narrative. For instance, additional pre-defined patterns may be created for detection and/or the textual summary may be tailored based on user preferences. In some such instances, one or more of the user preferences may be automatically determined by the personalized graph summarizer without requiring the user to explicitly indicate them. Embodiments may integrate machine learning and computer vision concepts.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. §119(e)to U.S. Provisional Application Ser. No. 62/353,222 filed Jun. 22, 2016,the entirety of which is incorporated herein by reference.

BACKGROUND

Generally, data visualizations may refer to various techniques used tocommunicate data or information by encoding it as visual objects (e.g.,points, lines, bars, etc.) contained in graphics. Typically, a datavisualization includes information that has been abstracted in someschematic form, and may include attributes or variables for the units ofinformation. For instance, numerical data may be encoded using dots,lines, or bars, to visually communicate a quantitative message.Sometimes portions of a data visualization may include information thatmay be of particular interest, such as a spike in values.

SUMMARY

This summary is not intended to identify only key or essential featuresof the described subject matter, nor is it intended to be used inisolation to determine the scope of the described subject matter. Thesubject matter should be understood by reference to appropriate portionsof the entire specification of this patent, any or all drawings, andeach claim.

Various embodiments described herein may include an apparatus comprisinga processor and a storage to store instructions that, when executed bythe processor, may cause the processor to perform operations comprisingone or more of: identify a data visualization comprising a graph image;determine a set of graph-type correlation scores for the graph image,the set of graph-type correlation scores to include a graph-typecorrelation score for each graph type of a plurality of graph types,each graph-type correlation score based on a comparison of at least aportion of the graph image with one or more graph-type models associatedwith each graph type of the plurality of graph types; evaluate the setof graph-type correlation scores to identify a graph type of the graphimage; retrieve a set of patterns based on the graph type of the graphimage, each pattern in the set of patterns to include one or morepattern examples; determine a set of region of interest correlationscores for the graph image based on matching the one or more patternexamples of each pattern in the set of patterns with at least a portionof the graph image, the set of region of interest correlation scores toinclude at least one region of interest correlation score for eachpattern in the set of patterns; evaluate the set of region of interestcorrelation scores to identify one or more candidate regions of interestof the graph image, each of the one or more candidate regions ofinterest to include a portion of the graph image; retrieve a set ofpattern models based on the set of candidate regions of interest of thegraph image, each candidate region of interest in the set of candidateregions of interest associated with one pattern model in the set ofpattern models, and each pattern model in the set of pattern modelsassociated with one pattern in the set of patterns; compare eachcandidate region of interest in the set of candidate regions of interestto an associated pattern model in the set of pattern models to determinea set of pattern model correlation scores, the set of pattern modelcorrelation scores to include a pattern model correlation score for eachcandidate region of interest of the one or more candidate regions ofinterest; identify one or more detected patterns based on the set ofpattern model correlation scores; retrieve one or more text templatesbased on the one or more detected patterns, the one or more texttemplates to include at least one portion of text associated with eachdetected pattern of the one or more detected patterns, each texttemplate of the one or more text templates associated with a prioritylevel; arrange the one or more text templates in an order based on thepriority level associated with each text template to generate a textualdescription of the graph image; and produce a personalized summary ofthe graph image, the summary of the graph image comprising the graphimage and the textual description of the graph image.

In some embodiments, the processor of the apparatus may be caused toperform operations comprising one or more of: detect a portion of thegraph image with contextual information; extract a textual element fromthe portion of the graph image with contextual information; and insertat least a portion of the textual element extracted from the portion ofthe graph image with contextual information into at least one texttemplate of the one or more text templates to generate the textualdescription of the graph image.

In one or more embodiments, the processor of the apparatus may be causedto perform operations comprising one or more of: identify a component ofthe graph image based on the graph type; detect a portion of the graphimage with potential contextual information; and determine contextualinformation is absent from the portion of the graph image with potentialcontextual information based on the component of the graph imageidentified based on the graph type.

In various embodiments, matching a pattern example of a pattern in theset of patterns with at least a portion of the graph image may compriseone or more of: overlay at least a portion of the pattern example on thegraph image in a plurality of positions; and compute a region ofinterest correlation score in the set of region of interest correlationscores for each of the plurality of positions.

In some embodiments, the processor of the apparatus may be caused toperform operations comprising one or more of: receive an additionalpattern example; and update a pattern model in the set of pattern modelsbased on the additional pattern example.

In one or more embodiments, each pattern model correlation score mayindicate a likelihood of a respective candidate region of interest ofthe one or more candidate regions of interest including an associatedpattern.

In various embodiments, the processor of the apparatus may be caused toperform operations comprising one or more of: present the one or moretext templates arranged based on the priority level associated with eachtemplate sentence via a user interface; arrange the one or more texttemplates in an updated order based on input received via the userinterface; alter a priority level of at least one of the one or moretext templates based on the updated order; and generate the textualdescription of the graph image based on the priority level associatedwith each text template, the priority level associated with each texttemplate to include the priority level of the at least one of the one ormore text templates altered based on the updated order.

In some embodiments, the processor of the apparatus may be caused toperform operations comprising: alter the priority level of a texttemplate based on the input received via a user interface.

In one or more embodiments, at least one pattern in the set of patternsmay comprise a personalized pattern. In one or more such embodiments,the processor of the apparatus may be cause to perform operationscomprising create the personalized pattern based on one or more examplegraph images and one or more pattern examples identified in the examplegraph images based on input received via a user interface.

In various embodiments, the processor of the apparatus may be caused toperform operations comprising associate one or more of a priority level,a template sentence, or a graph type with the personalized pattern basedon input received via the user interface.

Some embodiments described herein may include a computer-implementedmethod, comprising one or more of: identifying a data visualizationcomprising a graph image; determining a set of graph-type correlationscores for the graph image, the set of graph-type correlation scores toinclude a graph-type correlation score for each graph type of aplurality of graph types, each graph-type correlation score based on acomparison of at least a portion of the graph image with one or moregraph-type models associated with each graph type of the plurality ofgraph types; evaluating the set of graph-type correlation scores toidentify a graph type of the graph image; retrieving a set of patternsbased on the graph type of the graph image, each pattern in the set ofpatterns to include one or more pattern examples; determining a set ofregion of interest correlation scores for the graph image based onmatching the one or more pattern examples of each pattern in the set ofpatterns with at least a portion of the graph image, the set of regionof interest correlation scores to include at least one region ofinterest correlation score for each pattern in the set of patterns;evaluating the set of region of interest correlation scores to identifyone or more candidate regions of interest of the graph image, each ofthe one or more candidate regions of interest to include a portion ofthe graph image; retrieving a set of pattern models based on the set ofcandidate regions of interest of the graph image, each candidate regionof interest in the set of candidate regions of interest associated withone pattern model in the set of pattern models, and each pattern modelin the set of pattern models associated with one pattern in the set ofpatterns; comparing each candidate region of interest in the set ofcandidate regions of interest to an associated pattern model in the setof pattern models to determine a set of pattern model correlationscores, the set of pattern model correlation scores to include a patternmodel correlation score for each candidate region of interest of the oneor more candidate regions of interest; identifying one or more detectedpatterns based on the set of pattern model correlation scores;retrieving one or more text templates based on the one or more detectedpatterns, the one or more text templates to include at least one portionof text associated with each detected pattern of the one or moredetected patterns, each text template of the one or more text templatesassociated with a priority level; arranging the one or more texttemplates in an order based on the priority level associated with eachtext template to generate a textual description of the graph image; andgenerating a personalized summary of the graph image, the summary of thegraph image comprising the graph image and the textual description ofthe graph image.

In various embodiments, the computer-implemented method may include oneor more of: detecting a portion of the graph image with contextualinformation; extracting a textual element from the portion of the graphimage with contextual information; and inserting at least a portion ofthe textual element extracted from the portion of the graph image withcontextual information into at least one text template of the one ormore text templates to generate the textual description of the graphimage.

In one or more embodiments, the computer-implemented method may includeone or more of: identifying a component of the graph image based on thegraph type; detecting a portion of the graph image with potentialcontextual information; and determining contextual information is absentfrom the portion of the graph image with potential contextualinformation based on the component of the graph image identified basedon the graph type.

In some embodiments, matching a pattern example of a pattern in the setof patterns with at least a portion of the graph image may comprise oneor more of: overlaying at least a portion of the pattern example on thegraph image in a plurality of positions; and computing a region ofinterest correlation score in the set of region of interest correlationscores for each of the plurality of positions.

In various embodiments, the computer-implemented method may include oneor more of: receiving an additional pattern example; and updating apattern model in the set of pattern models based on the additionalpattern example.

In one or more embodiments, each pattern model correlation score mayindicate a likelihood of a respective candidate region of interest ofthe one or more candidate regions of interest including an associatedpattern.

In some embodiments, the computer-implemented method may include one ormore of: presenting the one or more text templates arranged based on thepriority level associated with each template sentence via a userinterface; arranging the one or more text templates in an updated orderbased on input received via the user interface; altering a prioritylevel of at least one of the one or more text templates based on theupdated order; and generating the textual description of the graph imagebased on the priority level associated with each text template, thepriority level associated with each text template to include thepriority level of the at least one of the one or more text templatesaltered based on the updated order.

In various embodiments, the computer-implemented method may includealtering the priority level of a text template based on the inputreceived via a user interface.

In one or more embodiments, at least one pattern in the set of patternscomprising a personalized pattern. In one or more such embodiments, thecomputer-implemented method may include creating the personalizedpattern based on one or more example graph images and one or morepattern examples identified in the example graph images based on inputreceived via a user interface.

In some embodiments, the computer-implemented method may includeassociating one or more of a priority level, a template sentence, or agraph type with the personalized pattern based on input received via theuser interface.

Various embodiments described herein may include a computer-programproduct tangibly embodied in a non-transitory machine-readable storagemedium, the computer-program product including instructions operable tocause a processor to perform operations comprising one or more of:identify a data visualization comprising a graph image; determine a setof graph-type correlation scores for the graph image, the set ofgraph-type correlation scores to include a graph-type correlation scorefor each graph type of a plurality of graph types, each graph-typecorrelation score based on a comparison of at least a portion of thegraph image with one or more graph-type models associated with eachgraph type of the plurality of graph types; evaluate the set ofgraph-type correlation scores to identify a graph type of the graphimage; retrieve a set of patterns based on the graph type of the graphimage, each pattern in the set of patterns to include one or morepattern examples; determine a set of region of interest correlationscores for the graph image based on matching the one or more patternexamples of each pattern in the set of patterns with at least a portionof the graph image, the set of region of interest correlation scores toinclude at least one region of interest correlation score for eachpattern in the set of patterns; evaluate the set of region of interestcorrelation scores to identify one or more candidate regions of interestof the graph image, each of the one or more candidate regions ofinterest to include a portion of the graph image; retrieve a set ofpattern models based on the set of candidate regions of interest of thegraph image, each candidate region of interest in the set of candidateregions of interest associated with one pattern model in the set ofpattern models, and each pattern model in the set of pattern modelsassociated with one pattern in the set of patterns; compare eachcandidate region of interest in the set of candidate regions of interestto an associated pattern model in the set of pattern models to determinea set of pattern model correlation scores, the set of pattern modelcorrelation scores to include a pattern model correlation score for eachcandidate region of interest of the one or more candidate regions ofinterest; identify one or more detected patterns based on the set ofpattern model correlation scores; retrieve one or more text templatesbased on the one or more detected patterns, the one or more texttemplates to include at least one portion of text associated with eachdetected pattern of the one or more detected patterns, each texttemplate of the one or more text templates associated with a prioritylevel; arrange the one or more text templates in an order based on thepriority level associated with each text template to generate a textualdescription of the graph image; and generate a personalized summary ofthe graph image, the summary of the graph image comprising the graphimage and the textual description of the graph image.

In some embodiments, the computer-program product may includeinstructions operable to cause the processor to perform operationscomprising one or more of: detect a portion of the graph image withcontextual information; extract a textual element from the portion ofthe graph image with contextual information; and insert at least aportion of the textual element extracted from the portion of the graphimage with contextual information into at least one text template of theone or more text templates to generate the textual description of thegraph image.

In one or more embodiments, the computer-program product may includeinstructions operable to cause the processor to perform operationscomprising one or more of: identify a component of the graph image basedon the graph type; detect a portion of the graph image with potentialcontextual information; and determine contextual information is absentfrom the portion of the graph image with potential contextualinformation based on the component of the graph image identified basedon the graph type.

In various embodiments, to match a pattern example of a pattern in theset of patterns with at least a portion of the graph image, thecomputer-program product may include instructions operable to cause theprocessor to perform operations comprising one or more of: overlay atleast a portion of the pattern example on the graph image in a pluralityof positions; and compute a region of interest correlation score in theset of region of interest correlation scores for each of the pluralityof positions.

In some embodiments, the computer-program product may includeinstructions operable to cause the processor to perform operationscomprising one or more of: receive an additional pattern example; andupdate a pattern model in the set of pattern models based on theadditional pattern example.

In one or more embodiments, each pattern model correlation score mayindicate a likelihood of a respective candidate region of interest ofthe one or more candidate regions of interest including an associatedpattern.

In various embodiments, the computer-program product may includeinstructions operable to cause the processor to perform operationscomprising one or more of: present the one or more text templatesarranged based on the priority level associated with each templatesentence via a user interface; arrange the one or more text templates inan updated order based on input received via the user interface; alter apriority level of at least one of the one or more text templates basedon the updated order; and generate the textual description of the graphimage based on the priority level associated with each text template,the priority level associated with each text template to include thepriority level of the at least one of the one or more text templatesaltered based on the updated order.

In some embodiments, the computer-program product may includeinstructions operable to cause the processor to perform operationscomprising alter the priority level of a text template based on theinput received via a user interface.

In one or more embodiments, at least one pattern in the set of patternsmay comprise a personalized pattern. In one or more such embodiments,the computer-program product may include instructions operable to causethe processor to perform operations comprising: create the personalizedpattern based on one or more example graph images and one or morepattern examples identified in the example graph images based on inputreceived via a user interface.

In various embodiments, the computer-program product may includeinstructions operable to cause the processor to perform operationscomprising: associate one or more of a priority level, a templatesentence, or a graph type with the personalized pattern based on inputreceived via the user interface.

The foregoing, together with other features and embodiments, will becomemore apparent upon referring to the following specification, claims, andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is described in conjunction with the appendedfigures:

FIG. 1 illustrates a block diagram that provides an illustration of thehardware components of a computing system, according to some embodimentsof the present technology.

FIG. 2 illustrates an example network including an example set ofdevices communicating with each other over an exchange system and via anetwork, according to some embodiments of the present technology.

FIG. 3 illustrates a representation of a conceptual model of acommunications protocol system, according to some embodiments of thepresent technology.

FIG. 4 illustrates a communications grid computing system including avariety of control and worker nodes, according to some embodiments ofthe present technology.

FIG. 5 illustrates a flow chart showing an example process for adjustinga communications grid or a work project in a communications grid after afailure of a node, according to some embodiments of the presenttechnology.

FIG. 6 illustrates a portion of a communications grid computing systemincluding a control node and a worker node, according to someembodiments of the present technology.

FIG. 7 illustrates a flow chart showing an example process for executinga data analysis or processing project, according to some embodiments ofthe present technology.

FIG. 8 illustrates a block diagram including components of an EventStream Processing Engine (ESPE), according to embodiments of the presenttechnology.

FIG. 9 illustrates a flow chart showing an example process includingoperations performed by an event stream processing engine, according tosome embodiments of the present technology.

FIG. 10 illustrates an ESP system interfacing between a publishingdevice and multiple event subscribing devices, according to embodimentsof the present technology.

FIG. 11A illustrates a flow chart showing an example process forgenerating and using a machine-learning model, according to someembodiments of the present technology.

FIG. 11B illustrates a neural network including multiple layers ofinterconnected neurons, according to some embodiments of the presenttechnology.

FIG. 12A illustrates an embodiment of an exemplary operating environmentfor a personalized graph summarizer.

FIG. 12B illustrates an example processing flow of a personalized graphsummarizer.

FIGS. 13A-13H illustrate an example processing flow of a personalizedpattern creator.

FIGS. 14A-14G illustrates an example processing flow of a visual patterndetector.

FIG. 15 illustrates an example processing flow of a summary generator.

FIG. 16 illustrates an example processing flow of a context extractor.

FIG. 17 illustrates an example processing flow of a summarypersonalizer.

FIG. 18 illustrates an embodiment of a personalized summary

FIGS. 19A-19B illustrates an embodiment of a logic flow.

DETAILED DESCRIPTION

Various embodiments are generally directed to systems for summarizingdata visualizations (i.e., images of data visualizations), such as agraph image, for instance. Some embodiments are particularly directed toa personalized graph summarizer that analyzes a data visualization, orimage, to detect pre-defined patterns within the data visualization, andproduces a textual summary of the data visualization based on thepre-defined patterns detected within the data visualization. In variousembodiments, the personalized graph summarizer may include features toadapt to the preferences of a user, thus providing a personalizedcomputer-generated narrative. For instance, additional pre-definedpatterns may be created for detection and/or the textual summary may betailored based on user preferences. In some such instances, one or moreof the user preferences may be automatically determined by thepersonalized graph summarizer without requiring the user to explicitlyindicate them. This and other embodiments are described and claimed.

Some challenges facing systems for summarizing data visualizationsinclude the inability to provide a meaningful summary tailored to thepreferences of a user. These challenges may result from the inputsrequired by systems to summarize data visualizations. For example,systems may require annotations of a data visualization as inputs. In afurther example, various systems may require a data file that includesthe underlying data or information to be communicated by a datavisualization in order to summarize the data visualization. It will beappreciated, as used herein a data visualization (i.e. image of a datavisualization) may include or refer to image data or an image file(e.g., Joint Photographic Experts Group (JPEG), Portable NetworkGraphics (PNG), graphic interchange format (GIF), Scalable VectorGraphics (SVG), and other image file formats), however, a datavisualization is separate and distinct from a data file that includesthe underlying data or information to be communicated by the datavisualization (e.g., Comma-Separated Values (CSV), Extensible MarkupLanguage (XML), Data Interchange Format (DIF), Excel Binary File Format(XLS), and similar file formats). For instance, an image file mayinclude pixel data for displaying an image of a scatter graph, while adata file may include numerical values corresponding to points in thescatter graph.

Adding further complexity, the types of data visualizations and thepatterns therein that need to be detected and summarized may vary amongusers. For example, industry-specific patterns may need to be identifiedand summarized. Further, different emphasis may be placed on differentportions of a data visualization by different users. For instance, oneuser may place more emphasis on upward trends, while another user placesmore emphasis on downward trends. These and other factors may result insystems for summarizing data visualizations with poor performance andlimited capabilities. An additional source of complexity includes theinability to provide relevant, informative, and/or customized summariesof data visualizations. For example, some systems may summarize a datavisualizations by merely restating the title of the data visualization.Such limitations can drastically reduce the usability and applicabilityof the data visualization summaries, contributing to inefficient systemswith limited flexibility.

Various embodiments described herein include a personalized graphsummarizer that can generate relevant and useful summaries of datavisualizations without relying on annotations or data files that includeunderlying data or information to be communicated by the datavisualization. For instance, the personalized graph summarizer maygenerate a natural-language textual summary of a data visualizationbased on pre-defined patterns detected in an image file that comprisesthe data visualization. In some embodiments, the personalized graphsummarizer may be able to learn additional types of data visualizationsand/or patterns to detect therein. For example, a personalized graphsummarizer may learn to identify and summarize a candlestick chart. Inone or more embodiments, the personalized graph summarizer may be ableto generate and/or tailor summaries of data visualizations based on userpreferences. In one or more such embodiments, the personalized graphsummarizer may learn user preferences based on interactions of the userwith the personalized graph summarizer. For instance, the personalizedgraph summarizer may order one or more sentences in a summary based onrevisions made by the user to a previous summary generated for aprevious data visualization. In various embodiments, the personalizedgraph summarizer may include the ability to extract context from a datavisualization. In various such embodiments, the personalized graphsummarizer may tailor a summary of a data visualization based on contextextracted from the data visualization. For example, axis-labels may beextracted from a data visualization and used to include units (e.g.,dollars, years, etc.) in a summary of the data visualization. In someembodiments, a personalized computer-generated narrative can beautomatically generated for one or more data visualizations.

In these and other ways the personalized graph summarizer may enablecustomized, efficient, and accurate detection of patterns in a datavisualization to provide relevant and useful summaries of the datavisualization, resulting in several technical effects and advantages. Invarious embodiments, the personalized graph summarizer may beimplemented via one or more computing devices, and thereby provideadditional and useful functionality to the one or more computingdevices, resulting in more capable and better functioning computingdevices. For example, the personalized graph summarizer may enable acomputing device to assist the visually impaired with interpreting andunderstanding data visualizations. One or more embodiments can involvecomputer vision.

With general reference to notations and nomenclature used herein,portions of the detailed description that follows may be presented interms of program procedures executed by a processor of a machine or ofmultiple networked machines. These procedural descriptions andrepresentations are used by those skilled in the art to most effectivelyconvey the substance of their work to others skilled in the art. Aprocedure is here, and generally, conceived to be a self-consistentsequence of operations leading to a desired result. These operations arethose requiring physical manipulations of physical quantities. Usually,though not necessarily, these quantities take the form of electrical,magnetic or optical communications capable of being stored, transferred,combined, compared, and otherwise manipulated. It proves convenient attimes, principally for reasons of common usage, to refer to what iscommunicated as bits, values, elements, symbols, characters, terms,numbers, or the like. It should be noted, however, that all of these andsimilar terms are to be associated with the appropriate physicalquantities and are merely convenient labels applied to those quantities.

Further, these manipulations are often referred to in terms, such asadding or comparing, which are commonly associated with mentaloperations performed by a human operator. However, no such capability ofa human operator is necessary, or desirable in most cases, in any of theoperations described herein that form part of one or more embodiments.Rather, these operations are machine operations. Useful machines forperforming operations of various embodiments include machinesselectively activated or configured by a routine stored within that iswritten in accordance with the teachings herein, and/or includeapparatus specially constructed for the required purpose. Variousembodiments also relate to apparatus or systems for performing theseoperations. These apparatuses may be specially constructed for therequired purpose or may include a general-purpose computer. The requiredstructure for a variety of these machines will appear from thedescription given.

Reference is now made to the drawings, wherein like reference numeralsare used to refer to like elements throughout. In the followingdescription, for purposes of explanation, numerous specific details areset forth in order to provide a thorough understanding thereof. It maybe evident, however, that the novel embodiments can be practiced withoutthese specific details. In other instances, well known structures anddevices are shown in block diagram form in order to facilitate adescription thereof. The intention is to cover all modifications,equivalents, and alternatives within the scope of the claims.

Systems depicted in some of the figures may be provided in variousconfigurations. In some embodiments, the systems may be configured as adistributed system where one or more components of the system aredistributed across one or more networks in a cloud computing systemand/or a fog computing system.

FIG. 1 is a block diagram that provides an illustration of the hardwarecomponents of a data transmission network 100, according to embodimentsof the present technology. Data transmission network 100 is aspecialized computer system that may be used for processing largeamounts of data where a large number of computer processing cycles arerequired.

Data transmission network 100 may also include computing environment114. Computing environment 114 may be a specialized computer or othermachine that processes the data received within the data transmissionnetwork 100. Data transmission network 100 also includes one or morenetwork devices 102. Network devices 102 may include client devices thatattempt to communicate with computing environment 114. For example,network devices 102 may send data to the computing environment 114 to beprocessed, may send signals to the computing environment 114 to controldifferent aspects of the computing environment or the data it isprocessing, among other reasons. Network devices 102 may interact withthe computing environment 114 through a number of ways, such as, forexample, over one or more networks 108. As shown in FIG. 1, computingenvironment 114 may include one or more other systems. For example,computing environment 114 may include a database system 118 and/or acommunications grid 120.

In other embodiments, network devices may provide a large amount ofdata, either all at once or streaming over a period of time (e.g., usingevent stream processing (ESP), described further with respect to FIGS.8-10), to the computing environment 114 via networks 108. For example,network devices 102 may include network computers, sensors, databases,or other devices that may transmit or otherwise provide data tocomputing environment 114. For example, network devices may includelocal area network devices, such as routers, hubs, switches, or othercomputer networking devices. These devices may provide a variety ofstored or generated data, such as network data or data specific to thenetwork devices themselves. Network devices may also include sensorsthat monitor their environment or other devices to collect dataregarding that environment or those devices, and such network devicesmay provide data they collect over time. Network devices may alsoinclude devices within the internet of things, such as devices within ahome automation network. Some of these devices may be referred to asedge devices, and may involve edge computing circuitry. Data may betransmitted by network devices directly to computing environment 114 orto network-attached data stores, such as network-attached data stores110 for storage so that the data may be retrieved later by the computingenvironment 114 or other portions of data transmission network 100.

Data transmission network 100 may also include one or morenetwork-attached data stores 110. Network-attached data stores 110 areused to store data to be processed by the computing environment 114 aswell as any intermediate or final data generated by the computing systemin non-volatile memory. However, in certain embodiments, theconfiguration of the computing environment 114 allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory (e.g., disk). This can be useful in certain situations, such aswhen the computing environment 114 receives ad hoc queries from a userand when responses, which are generated by processing large amounts ofdata, need to be generated on-the-fly. In this non-limiting situation,the computing environment 114 may be configured to retain the processedinformation within memory so that responses can be generated for theuser at different levels of detail as well as allow a user tointeractively query against this information.

Network-attached data stores may store a variety of different types ofdata organized in a variety of different ways and from a variety ofdifferent sources. For example, network-attached data storage mayinclude storage other than primary storage located within computingenvironment 114 that is directly accessible by processors locatedtherein. Network-attached data storage may include secondary, tertiaryor auxiliary storage, such as large hard drives, servers, virtualmemory, among other types. Storage devices may include portable ornon-portable storage devices, optical storage devices, and various othermediums capable of storing, containing data. A machine-readable storagemedium or computer-readable storage medium may include a non-transitorymedium in which data can be stored and that does not include carrierwaves and/or transitory electronic signals. Examples of a non-transitorymedium may include, for example, a magnetic disk or tape, opticalstorage media such as compact disk or digital versatile disk, flashmemory, memory or memory devices. A computer-program product may includecode and/or machine-executable instructions that may represent aprocedure, a function, a subprogram, a program, a routine, a subroutine,a module, a software package, a class, or any combination ofinstructions, data structures, or program statements. A code segment maybe coupled to another code segment or a hardware circuit by passingand/or receiving information, data, arguments, parameters, or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, amongothers. Furthermore, the data stores may hold a variety of differenttypes of data. For example, network-attached data stores 110 may holdunstructured (e.g., raw) data, such as manufacturing data (e.g., adatabase containing records identifying products being manufactured withparameter data for each product, such as colors and models) or productsales databases (e.g., a database containing individual data recordsidentifying details of individual product sales).

The unstructured data may be presented to the computing environment 114in different forms such as a flat file or a conglomerate of datarecords, and may have data values and accompanying time stamps. Thecomputing environment 114 may be used to analyze the unstructured datain a variety of ways to determine the best way to structure (e.g.,hierarchically) that data, such that the structured data is tailored toa type of further analysis that a user wishes to perform on the data.For example, after being processed, the unstructured time stamped datamay be aggregated by time (e.g., into daily time period units) togenerate time series data and/or structured hierarchically according toone or more dimensions (e.g., parameters, attributes, and/or variables).For example, data may be stored in a hierarchical data structure, suchas a ROLAP OR MOLAP database, or may be stored in another tabular form,such as in a flat-hierarchy form.

Data transmission network 100 may also include one or more server farms106. Computing environment 114 may route select communications or datato the one or more sever farms 106 or one or more servers within theserver farms. Server farms 106 can be configured to provide informationin a predetermined manner For example, server farms 106 may access datato transmit in response to a communication. Server farms 106 may beseparately housed from each other device within data transmissionnetwork 100, such as computing environment 114, and/or may be part of adevice or system.

Server farms 106 may host a variety of different types of dataprocessing as part of data transmission network 100. Server farms 106may receive a variety of different data from network devices, fromcomputing environment 114, from cloud network 116, or from othersources. The data may have been obtained or collected from one or moresensors, as inputs from a control database, or may have been received asinputs from an external system or device. Server farms 106 may assist inprocessing the data by turning raw data into processed data based on oneor more rules implemented by the server farms. For example, sensor datamay be analyzed to determine changes in an environment over time or inreal-time.

Data transmission network 100 may also include one or more cloudnetworks 116. Cloud network 116 may include a cloud infrastructuresystem that provides cloud services. In certain embodiments, servicesprovided by the cloud network 116 may include a host of services thatare made available to users of the cloud infrastructure system on demandCloud network 116 is shown in FIG. 1 as being connected to computingenvironment 114 (and therefore having computing environment 114 as itsclient or user), but cloud network 116 may be connected to or utilizedby any of the devices in FIG. 1. Services provided by the cloud networkcan dynamically scale to meet the needs of its users. The cloud network116 may comprise one or more computers, servers, and/or systems. In someembodiments, the computers, servers, and/or systems that make up thecloud network 116 are different from the user's own on-premisescomputers, servers, and/or systems. For example, the cloud network 116may host an application, and a user may, via a communication networksuch as the Internet, on demand, order and use the application.

While each device, server and system in FIG. 1 is shown as a singledevice, it will be appreciated that multiple devices may instead beused. For example, a set of network devices can be used to transmitvarious communications from a single user, or remote server 140 mayinclude a server stack. As another example, data may be processed aspart of computing environment 114.

Each communication within data transmission network 100 (e.g., betweenclient devices, between servers 106 and computing environment 114 orbetween a server and a device) may occur over one or more networks 108.Networks 108 may include one or more of a variety of different types ofnetworks, including a wireless network, a wired network, or acombination of a wired and wireless network. Examples of suitablenetworks include the Internet, a personal area network, a local areanetwork (LAN), a wide area network (WAN), or a wireless local areanetwork (WLAN). A wireless network may include a wireless interface orcombination of wireless interfaces. As an example, a network in the oneor more networks 108 may include a short-range communication channel,such as a Bluetooth or a Bluetooth Low Energy channel. A wired networkmay include a wired interface. The wired and/or wireless networks may beimplemented using routers, access points, bridges, gateways, or thelike, to connect devices in the network 114, as will be furtherdescribed with respect to FIG. 2. The one or more networks 108 can beincorporated entirely within or can include an intranet, an extranet, ora combination thereof. In one embodiment, communications between two ormore systems and/or devices can be achieved by a secure communicationsprotocol, such as secure sockets layer (SSL) or transport layer security(TLS). In addition, data and/or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things(e.g., machines, devices, phones, sensors) can be connected to networksand the data from these things can be collected and processed within thethings and/or external to the things. For example, the IoT can includesensors in many different devices, and high value analytics can beapplied to identify hidden relationships and drive increasedefficiencies. This can apply to both big data analytics and real-time(e.g., ESP) analytics. This will be described further below with respectto FIG. 2.

As noted, computing environment 114 may include a communications grid120 and a transmission network database system 118. Communications grid120 may be a grid-based computing system for processing large amounts ofdata. The transmission network database system 118 may be for managing,storing, and retrieving large amounts of data that are distributed toand stored in the one or more network-attached data stores 110 or otherdata stores that reside at different locations within the transmissionnetwork database system 118. The compute nodes in the grid-basedcomputing system 120 and the transmission network database system 118may share the same processor hardware, such as processors that arelocated within computing environment 114.

FIG. 2 illustrates an example network including an example set ofdevices communicating with each other over an exchange system and via anetwork, according to embodiments of the present technology. As noted,each communication within data transmission network 100 may occur overone or more networks. System 200 includes a network device 204configured to communicate with a variety of types of client devices, forexample client devices 230, over a variety of types of communicationchannels.

As shown in FIG. 2, network device 204 can transmit a communication overa network (e.g., a cellular network via a base station 210). Thecommunication can be routed to another network device, such as networkdevices 205-209, via base station 210. The communication can also berouted to computing environment 214 via base station 210. For example,network device 204 may collect data either from its surroundingenvironment or from other network devices (such as network devices205-209) and transmit that data to computing environment 214.

Although network devices 204-209 are shown in FIG. 2 as a mobile phone,laptop computer, tablet computer, temperature sensor, motion sensor, andaudio sensor respectively, the network devices may be or include sensorsthat are sensitive to detecting aspects of their environment. Forexample, the network devices may include sensors such as water sensors,power sensors, electrical current sensors, chemical sensors, opticalsensors, pressure sensors, geographic or position sensors (e.g., GPS),velocity sensors, acceleration sensors, flow rate sensors, among others.Examples of characteristics that may be sensed include force, torque,load, strain, position, temperature, air pressure, fluid flow, chemicalproperties, resistance, electromagnetic fields, radiation, irradiance,proximity, acoustics, moisture, distance, speed, vibrations,acceleration, electrical potential, electrical current, among others.The sensors may be mounted to various components used as part of avariety of different types of systems (e.g., an oil drilling operation).The network devices may detect and record data related to theenvironment that it monitors, and transmit that data to computingenvironment 214.

As noted, one type of system that may include various sensors thatcollect data to be processed and/or transmitted to a computingenvironment according to certain embodiments includes an oil drillingsystem. For example, the one or more drilling operation sensors mayinclude surface sensors that measure a hook load, a fluid rate, atemperature and a density in and out of the wellbore, a standpipepressure, a surface torque, a rotation speed of a drill pipe, a rate ofpenetration, a mechanical specific energy, etc. and downhole sensorsthat measure a rotation speed of a bit, fluid densities, downholetorque, downhole vibration (axial, tangential, lateral), a weightapplied at a drill bit, an annular pressure, a differential pressure, anazimuth, an inclination, a dog leg severity, a measured depth, avertical depth, a downhole temperature, etc. Besides the raw datacollected directly by the sensors, other data may include parameterseither developed by the sensors or assigned to the system by a client orother controlling device. For example, one or more drilling operationcontrol parameters may control settings such as a mud motor speed toflow ratio, a bit diameter, a predicted formation top, seismic data,weather data, etc. Other data may be generated using physical modelssuch as an earth model, a weather model, a seismic model, a bottom holeassembly model, a well plan model, an annular friction model, etc. Inaddition to sensor and control settings, predicted outputs, of forexample, the rate of penetration, mechanical specific energy, hook load,flow in fluid rate, flow out fluid rate, pump pressure, surface torque,rotation speed of the drill pipe, annular pressure, annular frictionpressure, annular temperature, equivalent circulating density, etc. mayalso be stored in the data warehouse.

In another example, another type of system that may include varioussensors that collect data to be processed and/or transmitted to acomputing environment according to certain embodiments includes a homeautomation or similar automated network in a different environment, suchas an office space, school, public space, sports venue, or a variety ofother locations. Network devices in such an automated network mayinclude network devices that allow a user to access, control, and/orconfigure various home appliances located within the user's home (e.g.,a television, radio, light, fan, humidifier, sensor, microwave, iron,and/or the like), or outside of the user's home (e.g., exterior motionsensors, exterior lighting, garage door openers, sprinkler systems, orthe like). For example, network device 102 may include a home automationswitch that may be coupled with a home appliance. In another embodiment,a network device can allow a user to access, control, and/or configuredevices, such as office-related devices (e.g., copy machine, printer, orfax machine), audio and/or video related devices (e.g., a receiver, aspeaker, a projector, a DVD player, or a television), media-playbackdevices (e.g., a compact disc player, a CD player, or the like),computing devices (e.g., a home computer, a laptop computer, a tablet, apersonal digital assistant (PDA), a computing device, or a wearabledevice), lighting devices (e.g., a lamp or recessed lighting), devicesassociated with a security system, devices associated with an alarmsystem, devices that can be operated in an automobile (e.g., radiodevices, navigation devices), and/or the like. Data may be collectedfrom such various sensors in raw form, or data may be processed by thesensors to create parameters or other data either developed by thesensors based on the raw data or assigned to the system by a client orother controlling device.

In another example, another type of system that may include varioussensors that collect data to be processed and/or transmitted to acomputing environment according to certain embodiments includes a poweror energy grid. A variety of different network devices may be includedin an energy grid, such as various devices within one or more powerplants, energy farms (e.g., wind farm, solar farm, among others) energystorage facilities, factories, homes and businesses of consumers, amongothers. One or more of such devices may include one or more sensors thatdetect energy gain or loss, electrical input or output or loss, and avariety of other efficiencies. These sensors may collect data to informusers of how the energy grid, and individual devices within the grid,may be functioning and how they may be made more efficient.

Network device sensors may also perform processing on data it collectsbefore transmitting the data to the computing environment 114, or beforedeciding whether to transmit data to the computing environment 114. Forexample, network devices may determine whether data collected meetscertain rules, for example by comparing data or values computed from thedata and comparing that data to one or more thresholds. The networkdevice may use this data and/or comparisons to determine if the datashould be transmitted to the computing environment 214 for further useor processing.

Computing environment 214 may include machines 220 and 240. Althoughcomputing environment 214 is shown in FIG. 2 as having two machines, 220and 240, computing environment 214 may have only one machine or may havemore than two machines. The machines that make up computing environment214 may include specialized computers, servers, or other machines thatare configured to individually and/or collectively process large amountsof data. The computing environment 214 may also include storage devicesthat include one or more databases of structured data, such as dataorganized in one or more hierarchies, or unstructured data. Thedatabases may communicate with the processing devices within computingenvironment 214 to distribute data to them. Since network devices maytransmit data to computing environment 214, that data may be received bythe computing environment 214 and subsequently stored within thosestorage devices. Data used by computing environment 214 may also bestored in data stores 235, which may also be a part of or connected tocomputing environment 214.

Computing environment 214 can communicate with various devices via oneor more routers 225 or other inter-network or intra-network connectioncomponents. For example, computing environment 214 may communicate withdevices 230 via one or more routers 225. Computing environment 214 maycollect, analyze and/or store data from or pertaining to communications,client device operations, client rules, and/or user-associated actionsstored at one or more data stores 235. Such data may influencecommunication routing to the devices within computing environment 214,how data is stored or processed within computing environment 214, amongother actions.

Notably, various other devices can further be used to influencecommunication routing and/or processing between devices within computingenvironment 214 and with devices outside of computing environment 214.For example, as shown in FIG. 2, computing environment 214 may include aweb server 240. Thus, computing environment 214 can retrieve data ofinterest, such as client information (e.g., product information, clientrules, etc.), technical product details, news, current or predictedweather, and so on.

In addition to computing environment 214 collecting data (e.g., asreceived from network devices, such as sensors, and client devices orother sources) to be processed as part of a big data analytics project,it may also receive data in real time as part of a streaming analyticsenvironment. As noted, data may be collected using a variety of sourcesas communicated via different kinds of networks or locally. Such datamay be received on a real-time streaming basis. For example, networkdevices may receive data periodically from network device sensors as thesensors continuously sense, monitor and track changes in theirenvironments. Devices within computing environment 214 may also performpre-analysis on data it receives to determine if the data receivedshould be processed as part of an ongoing project. The data received andcollected by computing environment 214, no matter what the source ormethod or timing of receipt, may be processed over a period of time fora client to determine results data based on the client's needs andrules.

FIG. 3 illustrates a representation of a conceptual model of acommunications protocol system, according to embodiments of the presenttechnology. More specifically, FIG. 3 identifies operation of acomputing environment in an Open Systems Interaction model thatcorresponds to various connection components. The model 300 shows, forexample, how a computing environment, such as computing environment 314(or computing environment 214 in FIG. 2) may communicate with otherdevices in its network, and control how communications between thecomputing environment and other devices are executed and under whatconditions.

The model can include layers 302-314. The layers are arranged in astack. Each layer in the stack serves the layer one level higher than it(except for the application layer, which is the highest layer), and isserved by the layer one level below it (except for the physical layer,which is the lowest layer). The physical layer is the lowest layerbecause it receives and transmits raw bites of data, and is the farthestlayer from the user in a communications system. On the other hand, theapplication layer is the highest layer because it interacts directlywith a software application.

As noted, the model includes a physical layer 302. Physical layer 302represents physical communication, and can define parameters of thatphysical communication. For example, such physical communication maycome in the form of electrical, optical, or electromagnetic signals.Physical layer 302 also defines protocols that may controlcommunications within a data transmission network.

Link layer 304 defines links and mechanisms used to transmit (i.e.,move) data across a network. The link layer manages node-to-nodecommunications, such as within a grid computing environment. Link layer304 can detect and correct errors (e.g., transmission errors in thephysical layer 302). Link layer 304 can also include a media accesscontrol (MAC) layer and logical link control (LLC) layer.

Network layer 306 defines the protocol for routing within a network. Inother words, the network layer coordinates transferring data acrossnodes in a same network (e.g., such as a grid computing environment).Network layer 306 can also define the processes used to structure localaddressing within the network.

Transport layer 308 can manage the transmission of data and the qualityof the transmission and/or receipt of that data. Transport layer 308 canprovide a protocol for transferring data, such as, for example, aTransmission Control Protocol (TCP). Transport layer 308 can assembleand disassemble data frames for transmission. The transport layer canalso detect transmission errors occurring in the layers below it.

Session layer 310 can establish, maintain, and manage communicationconnections between devices on a network. In other words, the sessionlayer controls the dialogues or nature of communications between networkdevices on the network. The session layer may also establishcheckpointing, adjournment, termination, and restart procedures.

Presentation layer 312 can provide translation for communicationsbetween the application and network layers. In other words, this layermay encrypt, decrypt and/or format data based on data types and/orencodings known to be accepted by an application or network layer.

Application layer 314 interacts directly with software applications andend users, and manages communications between them. Application layer314 can identify destinations, local resource states or availabilityand/or communication content or formatting using the applications.

Intra-network connection components 322 and 324 are shown to operate inlower levels, such as physical layer 302 and link layer 304,respectively. For example, a hub can operate in the physical layer, aswitch can operate in the physical layer, and a router can operate inthe network layer. Inter-network connection components 326 and 328 areshown to operate on higher levels, such as layers 306-314. For example,routers can operate in the network layer and network devices can operatein the transport, session, presentation, and application layers.

As noted, a computing environment 314 can interact with and/or operateon, in various embodiments, one, more, all or any of the various layers.For example, computing environment 314 can interact with a hub (e.g.,via the link layer) so as to adjust which devices the hub communicateswith. The physical layer may be served by the link layer, so it mayimplement such data from the link layer. For example, the computingenvironment 314 may control which devices it will receive data from. Forexample, if the computing environment 314 knows that a certain networkdevice has turned off, broken, or otherwise become unavailable orunreliable, the computing environment 314 may instruct the hub toprevent any data from being transmitted to the computing environment 314from that network device. Such a process may be beneficial to avoidreceiving data that is inaccurate or that has been influenced by anuncontrolled environment. As another example, computing environment 314can communicate with a bridge, switch, router or gateway and influencewhich device within the system (e.g., system 200) the component selectsas a destination. In some embodiments, computing environment 314 caninteract with various layers by exchanging communications with equipmentoperating on a particular layer by routing or modifying existingcommunications. In another embodiment, such as in a grid computingenvironment, a node may determine how data within the environment shouldbe routed (e.g., which node should receive certain data) based oncertain parameters or information provided by other layers within themodel.

As noted, the computing environment 314 may be a part of acommunications grid environment, the communications of which may beimplemented as shown in the protocol of FIG. 3. For example, referringto FIG. 2, one or more of machines 220 and 240 may be part of acommunications grid computing environment. A gridded computingenvironment may be employed in a distributed system with non-interactiveworkloads where data resides in memory on the machines, or computenodes. In such an environment, analytic code, instead of a databasemanagement system, controls the processing performed by the nodes. Datais co-located by pre-distributing it to the grid nodes, and the analyticcode on each node loads the local data into memory. Each node may beassigned a particular task such as a portion of a processing project, orto organize or control other nodes within the grid.

FIG. 4 illustrates a communications grid computing system 400 includinga variety of control and worker nodes, according to embodiments of thepresent technology. Communications grid computing system 400 includesthree control nodes and one or more worker nodes. Communications gridcomputing system 400 includes control nodes 402, 404, and 406. Thecontrol nodes are communicatively connected via communication paths 451,453, and 455. Therefore, the control nodes may transmit information(e.g., related to the communications grid or notifications), to andreceive information from each other. Although communications gridcomputing system 400 is shown in FIG. 4 as including three controlnodes, the communications grid may include more or less than threecontrol nodes.

Communications grid computing system (or just “communications grid”) 400also includes one or more worker nodes. Shown in FIG. 4 are six workernodes 410-420. Although FIG. 4 shows six worker nodes, a communicationsgrid according to embodiments of the present technology may include moreor less than six worker nodes. The number of worker nodes included in acommunications grid may be dependent upon how large the project or dataset is being processed by the communications grid, the capacity of eachworker node, the time designated for the communications grid to completethe project, among others. Each worker node within the communicationsgrid 400 may be connected (wired or wirelessly, and directly orindirectly) to control nodes 402-406. Therefore, each worker node mayreceive information from the control nodes (e.g., an instruction toperform work on a project) and may transmit information to the controlnodes (e.g., a result from work performed on a project). Furthermore,worker nodes may communicate with each other (either directly orindirectly). For example, worker nodes may transmit data between eachother related to a job being performed or an individual task within ajob being performed by that worker node. However, in certainembodiments, worker nodes may not, for example, be connected(communicatively or otherwise) to certain other worker nodes. In anembodiment, worker nodes may only be able to communicate with thecontrol node that controls it, and may not be able to communicate withother worker nodes in the communications grid, whether they are otherworker nodes controlled by the control node that controls the workernode, or worker nodes that are controlled by other control nodes in thecommunications grid.

A control node may connect with an external device with which thecontrol node may communicate (e.g., a grid user, such as a server orcomputer, may connect to a controller of the grid). For example, aserver or computer may connect to control nodes and may transmit aproject or job to the node. The project may include a data set. The dataset may be of any size. Once the control node receives such a projectincluding a large data set, the control node may distribute the data setor projects related to the data set to be performed by worker nodes.Alternatively, for a project including a large data set, the data setmay be received or stored by a machine other than a control node (e.g.,a Hadoop data node employing Hadoop Distributed File System, or HDFS).

Control nodes may maintain knowledge of the status of the nodes in thegrid (i.e., grid status information), accept work requests from clients,subdivide the work across worker nodes, coordinate the worker nodes,among other responsibilities. Worker nodes may accept work requests froma control node and provide the control node with results of the workperformed by the worker node. A grid may be started from a single node(e.g., a machine, computer, server, etc.). This first node may beassigned or may start as the primary control node that will control anyadditional nodes that enter the grid.

When a project is submitted for execution (e.g., by a client or acontroller of the grid) it may be assigned to a set of nodes. After thenodes are assigned to a project, a data structure (i.e., a communicator)may be created. The communicator may be used by the project forinformation to be shared between the project code running on each node.A communication handle may be created on each node. A handle, forexample, is a reference to the communicator that is valid within asingle process on a single node, and the handle may be used whenrequesting communications between nodes.

A control node, such as control node 402, may be designated as theprimary control node. A server, computer or other external device mayconnect to the primary control node. Once the control node receives aproject, the primary control node may distribute portions of the projectto its worker nodes for execution. For example, when a project isinitiated on communications grid 400, primary control node 402 controlsthe work to be performed for the project in order to complete theproject as requested or instructed. The primary control node maydistribute work to the worker nodes based on various factors, such aswhich subsets or portions of projects may be completed most efficientlyand in the correct amount of time. For example, a worker node mayperform analysis on a portion of data that is already local (e.g.,stored on) the worker node. The primary control node also coordinatesand processes the results of the work performed by each worker nodeafter each worker node executes and completes its job. For example, theprimary control node may receive a result from one or more worker nodes,and the control node may organize (e.g., collect and assemble) theresults received and compile them to produce a complete result for theproject received from the end user.

Any remaining control nodes, such as control nodes 404 and 406, may beassigned as backup control nodes for the project. In an embodiment,backup control nodes may not control any portion of the project.Instead, backup control nodes may serve as a backup for the primarycontrol node and take over as primary control node if the primarycontrol node were to fail. If a communications grid were to include onlya single control node, and the control node were to fail (e.g., thecontrol node is shut off or breaks) then the communications grid as awhole may fail and any project or job being run on the communicationsgrid may fail and may not complete. While the project may be run again,such a failure may cause a delay (severe delay in some cases, such asovernight delay) in completion of the project. Therefore, a grid withmultiple control nodes, including a backup control node, may bebeneficial.

To add another node or machine to the grid, the primary control node mayopen a pair of listening sockets, for example. A socket may be used toaccept work requests from clients, and the second socket may be used toaccept connections from other grid nodes. The primary control node maybe provided with a list of other nodes (e.g., other machines, computers,servers) that will participate in the grid, and the role that each nodewill fill in the grid. Upon startup of the primary control node (e.g.,the first node on the grid), the primary control node may use a networkprotocol to start the server process on every other node in the grid.Command line parameters, for example, may inform each node of one ormore pieces of information, such as: the role that the node will have inthe grid, the host name of the primary control node, the port number onwhich the primary control node is accepting connections from peer nodes,among others. The information may also be provided in a configurationfile, transmitted over a secure shell tunnel, recovered from aconfiguration server, among others. While the other machines in the gridmay not initially know about the configuration of the grid, thatinformation may also be sent to each other node by the primary controlnode. Updates of the grid information may also be subsequently sent tothose nodes.

For any control node, other than the primary control node added to thegrid, the control node may open three sockets. The first socket mayaccept work requests from clients, the second socket may acceptconnections from other grid members, and the third socket may connect(e.g., permanently) to the primary control node. When a control node(e.g., primary control node) receives a connection from another controlnode, it first checks to see if the peer node is in the list ofconfigured nodes in the grid. If it is not on the list, the control nodemay clear the connection. If it is on the list, it may then attempt toauthenticate the connection. If authentication is successful, theauthenticating node may transmit information to its peer, such as theport number on which a node is listening for connections, the host nameof the node, information about how to authenticate the node, among otherinformation. When a node, such as the new control node, receivesinformation about another active node, it will check to see if italready has a connection to that other node. If it does not have aconnection to that node, it may then establish a connection to thatcontrol node.

Any worker node added to the grid may establish a connection to theprimary control node and any other control nodes on the grid. Afterestablishing the connection, it may authenticate itself to the grid(e.g., any control nodes, including both primary and backup, or a serveror user controlling the grid). After successful authentication, theworker node may accept configuration information from the control node.

When a node joins a communications grid (e.g., when the node is poweredon or connected to an existing node on the grid or both), the node isassigned (e.g., by an operating system of the grid) a universally uniqueidentifier (UUID). This unique identifier may help other nodes andexternal entities (devices, users, etc.) to identify the node anddistinguish it from other nodes. When a node is connected to the grid,the node may share its unique identifier with the other nodes in thegrid. Since each node may share its unique identifier, each node mayknow the unique identifier of every other node on the grid. Uniqueidentifiers may also designate a hierarchy of each of the nodes (e.g.,backup control nodes) within the grid. For example, the uniqueidentifiers of each of the backup control nodes may be stored in a listof backup control nodes to indicate an order in which the backup controlnodes will take over for a failed primary control node to become a newprimary control node. However, a hierarchy of nodes may also bedetermined using methods other than using the unique identifiers of thenodes. For example, the hierarchy may be predetermined, or may beassigned based on other predetermined factors.

The grid may add new machines at any time (e.g., initiated from anycontrol node). Upon adding a new node to the grid, the control node mayfirst add the new node to its table of grid nodes. The control node mayalso then notify every other control node about the new node. The nodesreceiving the notification may acknowledge that they have updated theirconfiguration information.

Primary control node 402 may, for example, transmit one or morecommunications to backup control nodes 404 and 406 (and, for example, toother control or worker nodes within the communications grid). Suchcommunications may be sent periodically, at fixed time intervals,between known fixed stages of the project's execution, among otherprotocols. The communications transmitted by primary control node 402may be of varied types and may include a variety of types ofinformation. For example, primary control node 402 may transmitsnapshots (e.g., status information) of the communications grid so thatbackup control node 404 always has a recent snapshot of thecommunications grid. The snapshot or grid status may include, forexample, the structure of the grid (including, for example, the workernodes in the grid, unique identifiers of the nodes, or theirrelationships with the primary control node) and the status of a project(including, for example, the status of each worker node's portion of theproject). The snapshot may also include analysis or results receivedfrom worker nodes in the communications grid. The backup control nodesmay receive and store the backup data received from the primary controlnode. The backup control nodes may transmit a request for such asnapshot (or other information) from the primary control node, or theprimary control node may send such information periodically to thebackup control nodes.

As noted, the backup data may allow the backup control node to take overas primary control node if the primary control node fails withoutrequiring the grid to start the project over from scratch. If theprimary control node fails, the backup control node that will take overas primary control node may retrieve the most recent version of thesnapshot received from the primary control node and use the snapshot tocontinue the project from the stage of the project indicated by thebackup data. This may prevent failure of the project as a whole.

A backup control node may use various methods to determine that theprimary control node has failed. In one example of such a method, theprimary control node may transmit (e.g., periodically) a communicationto the backup control node that indicates that the primary control nodeis working and has not failed, such as a heartbeat communication. Thebackup control node may determine that the primary control node hasfailed if the backup control node has not received a heartbeatcommunication for a certain predetermined period of time. Alternatively,a backup control node may also receive a communication from the primarycontrol node itself (before it failed) or from a worker node that theprimary control node has failed, for example because the primary controlnode has failed to communicate with the worker node.

Different methods may be performed to determine which backup controlnode of a set of backup control nodes (e.g., backup control nodes 404and 406) will take over for failed primary control node 402 and becomethe new primary control node. For example, the new primary control nodemay be chosen based on a ranking or “hierarchy” of backup control nodesbased on their unique identifiers. In an alternative embodiment, abackup control node may be assigned to be the new primary control nodeby another device in the communications grid or from an external device(e.g., a system infrastructure or an end user, such as a server orcomputer, controlling the communications grid). In another alternativeembodiment, the backup control node that takes over as the new primarycontrol node may be designated based on bandwidth or other statisticsabout the communications grid.

A worker node within the communications grid may also fail. If a workernode fails, work being performed by the failed worker node may beredistributed amongst the operational worker nodes. In an alternativeembodiment, the primary control node may transmit a communication toeach of the operable worker nodes still on the communications grid thateach of the worker nodes should purposefully fail also. After each ofthe worker nodes fail, they may each retrieve their most recent savedcheckpoint of their status and re-start the project from that checkpointto minimize lost progress on the project being executed.

FIG. 5 illustrates a flow chart showing an example process for adjustinga communications grid or a work project in a communications grid after afailure of a node, according to embodiments of the present technology.The process may include, for example, receiving grid status informationincluding a project status of a portion of a project being executed by anode in the communications grid, as described in operation 502. Forexample, a control node (e.g., a backup control node connected to aprimary control node and a worker node on a communications grid) mayreceive grid status information, where the grid status informationincludes a project status of the primary control node or a projectstatus of the worker node. The project status of the primary controlnode and the project status of the worker node may include a status ofone or more portions of a project being executed by the primary andworker nodes in the communications grid. The process may also includestoring the grid status information, as described in operation 504. Forexample, a control node (e.g., a backup control node) may store thereceived grid status information locally within the control node.Alternatively, the grid status information may be sent to another devicefor storage where the control node may have access to the information.

The process may also include receiving a failure communicationcorresponding to a node in the communications grid in operation 506. Forexample, a node may receive a failure communication including anindication that the primary control node has failed, prompting a backupcontrol node to take over for the primary control node. In analternative embodiment, a node may receive a failure that a worker nodehas failed, prompting a control node to reassign the work beingperformed by the worker node. The process may also include reassigning anode or a portion of the project being executed by the failed node, asdescribed in operation 508. For example, a control node may designatethe backup control node as a new primary control node based on thefailure communication upon receiving the failure communication. If thefailed node is a worker node, a control node may identify a projectstatus of the failed worker node using the snapshot of thecommunications grid, where the project status of the failed worker nodeincludes a status of a portion of the project being executed by thefailed worker node at the failure time.

The process may also include receiving updated grid status informationbased on the reassignment, as described in operation 510, andtransmitting a set of instructions based on the updated grid statusinformation to one or more nodes in the communications grid, asdescribed in operation 512. The updated grid status information mayinclude an updated project status of the primary control node or anupdated project status of the worker node. The updated information maybe transmitted to the other nodes in the grid to update their stalestored information.

FIG. 6 illustrates a portion of a communications grid computing system600 including a control node and a worker node, according to embodimentsof the present technology. Communications grid 600 computing systemincludes one control node (control node 602) and one worker node (workernode 610) for purposes of illustration, but may include more workerand/or control nodes. The control node 602 is communicatively connectedto worker node 610 via communication path 650. Therefore, control node602 may transmit information (e.g., related to the communications gridor notifications), to and receive information from worker node 610 viapath 650.

Similar to in FIG. 4, communications grid computing system (or just“communications grid”) 600 includes data processing nodes (control node602 and worker node 610). Nodes 602 and 610 comprise multi-core dataprocessors. Each node 602 and 610 includes a grid-enabled softwarecomponent (GESC) 620 that executes on the data processor associated withthat node and interfaces with buffer memory 622 also associated withthat node. Each node 602 and 610 includes a database management software(DBMS) 628 that executes on a database server (not shown) at controlnode 602 and on a database server (not shown) at worker node 610.

Each node also includes a data store 624. Data stores 624, similar tonetwork-attached data stores 110 in FIG. 1 and data stores 235 in FIG.2, are used to store data to be processed by the nodes in the computingenvironment. Data stores 624 may also store any intermediate or finaldata generated by the computing system after being processed, forexample in non-volatile memory. However, in certain embodiments, theconfiguration of the grid computing environment allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory. Storing such data in volatile memory may be useful in certainsituations, such as when the grid receives queries (e.g., ad hoc) from aclient and when responses, which are generated by processing largeamounts of data, need to be generated quickly or on-the-fly. In such asituation, the grid may be configured to retain the data within memoryso that responses can be generated at different levels of detail and sothat a client may interactively query against this information.

Each node also includes a user-defined function (UDF) 626. The UDFprovides a mechanism for the DMBS 628 to transfer data to or receivedata from the database stored in the data stores 624 that are managed bythe DBMS. For example, UDF 626 can be invoked by the DBMS to providedata to the GESC for processing. The UDF 626 may establish a socketconnection (not shown) with the GESC to transfer the data.Alternatively, the UDF 626 can transfer data to the GESC by writing datato shared memory accessible by both the UDF and the GESC.

The GESC 620 at the nodes 602 and 620 may be connected via a network,such as network 108 shown in FIG. 1. Therefore, nodes 602 and 620 cancommunicate with each other via the network using a predeterminedcommunication protocol such as, for example, the Message PassingInterface (MPI). Each GESC 620 can engage in point-to-pointcommunication with the GESC at another node or in collectivecommunication with multiple GESCs via the network. The GESC 620 at eachnode may contain identical (or nearly identical) software instructions.The GESC at the control node 602 can communicate, over a communicationpath 652, with a client deice 630. More specifically, control node 602may communicate with client application 632 hosted by the client device630 to receive queries and to respond to those queries after processinglarge amounts of data.

DMBS 628 may control the creation, maintenance, and use of database ordata structure (not shown) within a node 602 or 610. The database mayorganize data stored in data stores 624. The DMBS 628 at control node602 may accept requests for data and transfer the appropriate data forthe request. With such a process, collections of data may be distributedacross multiple physical locations. In this example, each node 602 and610 stores a portion of the total data managed by the management systemin its associated data store 624.

Furthermore, the DBMS may be responsible for protecting against dataloss using replication techniques. Replication includes providing abackup copy of data stored on one node on one or more other nodes.Therefore, if one node fails, the data from the failed node can berecovered from a replicated copy residing at another node. However, asdescribed herein with respect to FIG. 4, data or status information foreach node in the communications grid may also be shared with each nodeon the grid.

FIG. 7 illustrates a flow chart showing an example method for executinga project within a grid computing system, according to embodiments ofthe present technology. As described with respect to FIG. 6, the GESC atthe control node may transmit data with a client device (e.g., clientdevice 630) to receive queries for executing a project and to respond tothose queries after large amounts of data have been processed. The querymay be transmitted to the control node, where the query may include arequest for executing a project, as described in operation 702. Thequery can contain instructions on the type of data analysis to beperformed in the project and whether the project should be executedusing the grid-based computing environment, as shown in operation 704.

To initiate the project, the control node may determine if the queryrequests use of the grid-based computing environment to execute theproject. If the determination is no, then the control node initiatesexecution of the project in a solo environment (e.g., at the controlnode), as described in operation 710. If the determination is yes, thecontrol node may initiate execution of the project in the grid-basedcomputing environment, as described in operation 706. In such asituation, the request may include a requested configuration of thegrid. For example, the request may include a number of control nodes anda number of worker nodes to be used in the grid when executing theproject. After the project has been completed, the control node maytransmit results of the analysis yielded by the grid, as described inoperation 708. Whether the project is executed in a solo or grid-basedenvironment, the control node provides the results of the project.

As noted with respect to FIG. 2, the computing environments describedherein may collect data (e.g., as received from network devices, such assensors, such as network devices 204-209 in FIG. 2, and client devicesor other sources) to be processed as part of a data analytics project,and data may be received in real time as part of a streaming analyticsenvironment (e.g., ESP). Data may be collected using a variety ofsources as communicated via different kinds of networks or locally, suchas on a real-time streaming basis. For example, network devices mayreceive data periodically from network device sensors as the sensorscontinuously sense, monitor and track changes in their environments.More specifically, an increasing number of distributed applicationsdevelop or produce continuously flowing data from distributed sources byapplying queries to the data before distributing the data togeographically distributed recipients. An event stream processing engine(ESPE) may continuously apply the queries to the data as it is receivedand determines which entities should receive the data. Client or otherdevices may also subscribe to the ESPE or other devices processing ESPdata so that they can receive data after processing, based on forexample the entities determined by the processing engine. For example,client devices 230 in FIG. 2 may subscribe to the ESPE in computingenvironment 214. In another example, event subscription devices 874 a-c,described further with respect to FIG. 10, may also subscribe to theESPE. The ESPE may determine or define how input data or event streamsfrom network devices or other publishers (e.g., network devices 204-209in FIG. 2) are transformed into meaningful output data to be consumed bysubscribers, such as for example client devices 230 in FIG. 2.

FIG. 8 illustrates a block diagram including components of an EventStream Processing Engine (ESPE), according to embodiments of the presenttechnology. ESPE 800 may include one or more projects 802. A project maybe described as a second-level container in an engine model managed byESPE 800 where a thread pool size for the project may be defined by auser. Each project of the one or more projects 802 may include one ormore continuous queries 804 that contain data flows, which are datatransformations of incoming event streams. The one or more continuousqueries 804 may include one or more source windows 806 and one or morederived windows 808.

The ESPE may receive streaming data over a period of time related tocertain events, such as events or other data sensed by one or morenetwork devices. The ESPE may perform operations associated withprocessing data created by the one or more devices. For example, theESPE may receive data from the one or more network devices 204-209 shownin FIG. 2. As noted, the network devices may include sensors that sensedifferent aspects of their environments, and may collect data over timebased on those sensed observations. For example, the ESPE may beimplemented within one or more of machines 220 and 240 shown in FIG. 2.The ESPE may be implemented within such a machine by an ESP application.An ESP application may embed an ESPE with its own dedicated thread poolor pools into its application space where the main application threadcan do application-specific work and the ESPE processes event streams atleast by creating an instance of a model into processing objects.

The engine container is the top-level container in a model that managesthe resources of the one or more projects 802. In an illustrativeembodiment, for example, there may be only one ESPE 800 for eachinstance of the ESP application, and ESPE 800 may have a unique enginename. Additionally, the one or more projects 802 may each have uniqueproject names, and each query may have a unique continuous query nameand begin with a uniquely named source window of the one or more sourcewindows 806. ESPE 800 may or may not be persistent.

Continuous query modeling involves defining directed graphs of windowsfor event stream manipulation and transformation. A window in thecontext of event stream manipulation and transformation is a processingnode in an event stream processing model. A window in a continuous querycan perform aggregations, computations, pattern-matching, and otheroperations on data flowing through the window. A continuous query may bedescribed as a directed graph of source, relational, pattern matching,and procedural windows. The one or more source windows 806 and the oneor more derived windows 808 represent continuously executing queriesthat generate updates to a query result set as new event blocks streamthrough ESPE 800. A directed graph, for example, is a set of nodesconnected by edges, where the edges have a direction associated withthem.

An event object may be described as a packet of data accessible as acollection of fields, with at least one of the fields defined as a keyor unique identifier (ID). The event object may be created using avariety of formats including binary, alphanumeric, XML, etc. Each eventobject may include one or more fields designated as a primary identifier(ID) for the event so ESPE 800 can support operation codes (opcodes) forevents including insert, update, upsert, and delete. Upsert opcodesupdate the event if the key field already exists; otherwise, the eventis inserted. For illustration, an event object may be a packed binaryrepresentation of a set of field values and include both metadata andfield data associated with an event. The metadata may include an opcodeindicating if the event represents an insert, update, delete, or upsert,a set of flags indicating if the event is a normal, partial-update, or aretention generated event from retention policy management, and a set ofmicrosecond timestamps that can be used for latency measurements.

An event block object may be described as a grouping or package of eventobjects. An event stream may be described as a flow of event blockobjects. A continuous query of the one or more continuous queries 804transforms a source event stream made up of streaming event blockobjects published into ESPE 800 into one or more output event streamsusing the one or more source windows 806 and the one or more derivedwindows 808. A continuous query can also be thought of as data flowmodeling.

The one or more source windows 806 are at the top of the directed graphand have no windows feeding into them. Event streams are published intothe one or more source windows 806, and from there, the event streamsmay be directed to the next set of connected windows as defined by thedirected graph. The one or more derived windows 808 are all instantiatedwindows that are not source windows and that have other windowsstreaming events into them. The one or more derived windows 808 mayperform computations or transformations on the incoming event streams.The one or more derived windows 808 transform event streams based on thewindow type (that is operators such as join, filter, compute, aggregate,copy, pattern match, procedural, union, etc.) and window settings. Asevent streams are published into ESPE 800, they are continuouslyqueried, and the resulting sets of derived windows in these queries arecontinuously updated.

FIG. 9 illustrates a flow chart showing an example process includingoperations performed by an event stream processing engine, according tosome embodiments of the present technology. As noted, the ESPE 800 (oran associated ESP application) defines how input event streams aretransformed into meaningful output event streams. More specifically, theESP application may define how input event streams from publishers(e.g., network devices providing sensed data) are transformed intomeaningful output event streams consumed by subscribers (e.g., a dataanalytics project being executed by a machine or set of machines).

Within the application, a user may interact with one or more userinterface windows presented to the user in a display under control ofthe ESPE independently or through a browser application in an orderselectable by the user. For example, a user may execute an ESPapplication, which causes presentation of a first user interface window,which may include a plurality of menus and selectors such as drop downmenus, buttons, text boxes, hyperlinks, etc. associated with the ESPapplication as understood by a person of skill in the art. As furtherunderstood by a person of skill in the art, various operations may beperformed in parallel, for example, using a plurality of threads.

At operation 900, an ESP application may define and start an ESPE,thereby instantiating an ESPE at a device, such as machine 220 and/or240. In an operation 902, the engine container is created. Forillustration, ESPE 800 may be instantiated using a function call thatspecifies the engine container as a manager for the model.

In an operation 904, the one or more continuous queries 804 areinstantiated by ESPE 800 as a model. The one or more continuous queries804 may be instantiated with a dedicated thread pool or pools thatgenerate updates as new events stream through ESPE 800. Forillustration, the one or more continuous queries 804 may be created tomodel business processing logic within ESPE 800, to predict eventswithin ESPE 800, to model a physical system within ESPE 800, to predictthe physical system state within ESPE 800, etc. For example, as noted,ESPE 800 may be used to support sensor data monitoring and management(e.g., sensing may include force, torque, load, strain, position,temperature, air pressure, fluid flow, chemical properties, resistance,electromagnetic fields, radiation, irradiance, proximity, acoustics,moisture, distance, speed, vibrations, acceleration, electricalpotential, or electrical current, etc.).

ESPE 800 may analyze and process events in motion or “event streams.”Instead of storing data and running queries against the stored data,ESPE 800 may store queries and stream data through them to allowcontinuous analysis of data as it is received. The one or more sourcewindows 806 and the one or more derived windows 808 may be created basedon the relational, pattern matching, and procedural algorithms thattransform the input event streams into the output event streams tomodel, simulate, score, test, predict, etc. based on the continuousquery model defined and application to the streamed data.

In an operation 906, a publish/subscribe (pub/sub) capability isinitialized for ESPE 800. In an illustrative embodiment, a pub/subcapability is initialized for each project of the one or more projects802. To initialize and enable pub/sub capability for ESPE 800, a portnumber may be provided. Pub/sub clients can use a host name of an ESPdevice running the ESPE and the port number to establish pub/subconnections to ESPE 800.

FIG. 10 illustrates an ESP system 850 interfacing between publishingdevice 872 and event subscribing devices 874 a-c, according toembodiments of the present technology. ESP system 850 may include ESPdevice or subsystem 851, event publishing device 872, an eventsubscribing device A 874 a, an event subscribing device B 874 b, and anevent subscribing device C 874 c. Input event streams are output to ESPdevice 851 by publishing device 872. In alternative embodiments, theinput event streams may be created by a plurality of publishing devices.The plurality of publishing devices further may publish event streams toother ESP devices. The one or more continuous queries instantiated byESPE 800 may analyze and process the input event streams to form outputevent streams output to event subscribing device A 874 a, eventsubscribing device B 874 b, and event subscribing device C 874 c. ESPsystem 850 may include a greater or a fewer number of event subscribingdevices of event subscribing devices.

Publish-subscribe is a message-oriented interaction paradigm based onindirect addressing. Processed data recipients specify their interest inreceiving information from ESPE 800 by subscribing to specific classesof events, while information sources publish events to ESPE 800 withoutdirectly addressing the receiving parties. ESPE 800 coordinates theinteractions and processes the data. In some cases, the data sourcereceives confirmation that the published information has been receivedby a data recipient.

A publish/subscribe API may be described as a library that enables anevent publisher, such as publishing device 872, to publish event streamsinto ESPE 800 or an event subscriber, such as event subscribing device A874 a, event subscribing device B 874 b, and event subscribing device C874 c, to subscribe to event streams from ESPE 800. For illustration,one or more publish/subscribe APIs may be defined. Using thepublish/subscribe API, an event publishing application may publish eventstreams into a running event stream processor project source window ofESPE 800, and the event subscription application may subscribe to anevent stream processor project source window of ESPE 800.

The publish/subscribe API provides cross-platform connectivity andendianness compatibility between ESP application and other networkedapplications, such as event publishing applications instantiated atpublishing device 872, and event subscription applications instantiatedat one or more of event subscribing device A 874 a, event subscribingdevice B 874 b, and event subscribing device C 874 c.

Referring back to FIG. 9, operation 906 initializes thepublish/subscribe capability of ESPE 800. In an operation 908, the oneor more projects 802 are started. The one or more started projects mayrun in the background on an ESP device. In an operation 910, an eventblock object is received from one or more computing device of the eventpublishing device 872.

ESP subsystem 800 may include a publishing client 852, ESPE 800, asubscribing client A 854, a subscribing client B 856, and a subscribingclient C 858. Publishing client 852 may be started by an eventpublishing application executing at publishing device 872 using thepublish/subscribe API. Subscribing client A 854 may be started by anevent subscription application A, executing at event subscribing deviceA 874 a using the publish/subscribe API. Subscribing client B 856 may bestarted by an event subscription application B executing at eventsubscribing device B 874 b using the publish/subscribe API. Subscribingclient C 858 may be started by an event subscription application Cexecuting at event subscribing device C 874 c using thepublish/subscribe API.

An event block object containing one or more event objects is injectedinto a source window of the one or more source windows 806 from aninstance of an event publishing application on event publishing device872. The event block object may be generated, for example, by the eventpublishing application and may be received by publishing client 852. Aunique ID may be maintained as the event block object is passed betweenthe one or more source windows 806 and/or the one or more derivedwindows 808 of ESPE 800, and to subscribing client A 854, subscribingclient B 806, and subscribing client C 808 and to event subscriptiondevice A 874 a, event subscription device B 874 b, and eventsubscription device C 874 c. Publishing client 852 may further generateand include a unique embedded transaction ID in the event block objectas the event block object is processed by a continuous query, as well asthe unique ID that publishing device 872 assigned to the event blockobject.

In an operation 912, the event block object is processed through the oneor more continuous queries 804. In an operation 914, the processed eventblock object is output to one or more computing devices of the eventsubscribing devices 874 a-c. For example, subscribing client A 804,subscribing client B 806, and subscribing client C 808 may send thereceived event block object to event subscription device A 874 a, eventsubscription device B 874 b, and event subscription device C 874 c,respectively.

ESPE 800 maintains the event block containership aspect of the receivedevent blocks from when the event block is published into a source windowand works its way through the directed graph defined by the one or morecontinuous queries 804 with the various event translations before beingoutput to subscribers. Subscribers can correlate a group of subscribedevents back to a group of published events by comparing the unique ID ofthe event block object that a publisher, such as publishing device 872,attached to the event block object with the event block ID received bythe subscriber.

In an operation 916, a determination is made concerning whether or notprocessing is stopped. If processing is not stopped, processingcontinues in operation 910 to continue receiving the one or more eventstreams containing event block objects from the, for example, one ormore network devices. If processing is stopped, processing continues inan operation 918. In operation 918, the started projects are stopped. Inoperation 920, the ESPE is shutdown.

As noted, in some embodiments, big data is processed for an analyticsproject after the data is received and stored. In other embodiments,distributed applications process continuously flowing data in real-timefrom distributed sources by applying queries to the data beforedistributing the data to geographically distributed recipients. Asnoted, an event stream processing engine (ESPE) may continuously applythe queries to the data as it is received and determines which entitiesreceive the processed data. This allows for large amounts of data beingreceived and/or collected in a variety of environments to be processedand distributed in real time. For example, as shown with respect to FIG.2, data may be collected from network devices that may include deviceswithin the internet of things, such as devices within a home automationnetwork. However, such data may be collected from a variety of differentresources in a variety of different environments. In any such situation,embodiments of the present technology allow for real-time processing ofsuch data.

Aspects of the current disclosure provide technical solutions totechnical problems, such as computing problems that arise when an ESPdevice fails which results in a complete service interruption andpotentially significant data loss. The data loss can be catastrophicwhen the streamed data is supporting mission critical operations such asthose in support of an ongoing manufacturing or drilling operation. Anembodiment of an ESP system achieves a rapid and seamless failover ofESPE running at the plurality of ESP devices without serviceinterruption or data loss, thus significantly improving the reliabilityof an operational system that relies on the live or real-time processingof the data streams. The event publishing systems, the event subscribingsystems, and each ESPE not executing at a failed ESP device are notaware of or effected by the failed ESP device. The ESP system mayinclude thousands of event publishing systems and event subscribingsystems. The ESP system keeps the failover logic and awareness withinthe boundaries of out-messaging network connector and out-messagingnetwork device.

In one example embodiment, a system is provided to support a failoverwhen event stream processing (ESP) event blocks. The system includes,but is not limited to, an out-messaging network device and a computingdevice. The computing device includes, but is not limited to, aprocessor and a computer-readable medium operably coupled to theprocessor. The processor is configured to execute an ESP engine (ESPE).The computer-readable medium has instructions stored thereon that, whenexecuted by the processor, cause the computing device to support thefailover. An event block object is received from the ESPE that includesa unique identifier. A first status of the computing device as active orstandby is determined. When the first status is active, a second statusof the computing device as newly active or not newly active isdetermined. Newly active is determined when the computing device isswitched from a standby status to an active status. When the secondstatus is newly active, a last published event block object identifierthat uniquely identifies a last published event block object isdetermined. A next event block object is selected from a non-transitorycomputer-readable medium accessible by the computing device. The nextevent block object has an event block object identifier that is greaterthan the determined last published event block object identifier. Theselected next event block object is published to an out-messagingnetwork device. When the second status of the computing device is notnewly active, the received event block object is published to theout-messaging network device. When the first status of the computingdevice is standby, the received event block object is stored in thenon-transitory computer-readable medium.

FIG. 11A is a flow chart of an example of a process for generating andusing a machine-learning model according to some aspects. Machinelearning is a branch of artificial intelligence that relates tomathematical models that can learn from, categorize, and makepredictions about data. Such mathematical models, which can be referredto as machine-learning models, can classify input data among two or moreclasses; cluster input data among two or more groups; predict a resultbased on input data; identify patterns or trends in input data; identifya distribution of input data in a space; or any combination of these.Examples of machine-learning models can include (i) neural networks;(ii) decision trees, such as classification trees and regression trees;(iii) classifiers, such as Naïve bias classifiers, logistic regressionclassifiers, ridge regression classifiers, random forest classifiers,least absolute shrinkage and selector (LASSO) classifiers, and supportvector machines; (iv) clusterers, such as k-means clusterers, mean-shiftclusterers, and spectral clusterers; (v) factorizers, such asfactorization machines, principal component analyzers and kernelprincipal component analyzers; and (vi) ensembles or other combinationsof machine-learning models. In some examples, neural networks caninclude deep neural networks, feed-forward neural networks, recurrentneural networks, convolutional neural networks, radial basis function(RBF) neural networks, echo state neural networks, long short-termmemory neural networks, bi-directional recurrent neural networks, gatedneural networks, hierarchical recurrent neural networks, stochasticneural networks, modular neural networks, spiking neural networks,dynamic neural networks, cascading neural networks, neuro-fuzzy neuralnetworks, or any combination of these.

Different machine-learning models may be used interchangeably to performa task. Examples of tasks that can be performed at least partially usingmachine-learning models include various types of scoring;bioinformatics; cheminformatics; software engineering; fraud detection;customer segmentation; generating online recommendations; adaptivewebsites; determining customer lifetime value; search engines; placingadvertisements in real time or near real time; classifying DNAsequences; affective computing; performing natural language processingand understanding; object recognition and computer vision; roboticlocomotion; playing games; optimization and metaheuristics; detectingnetwork intrusions; medical diagnosis and monitoring; or predicting whenan asset, such as a machine, will need maintenance.

Any number and combination of tools can be used to createmachine-learning models. Examples of tools for creating and managingmachine-learning models can include SAS® Enterprise Miner, SAS® RapidPredictive Modeler, and SAS® Model Manager, SAS Cloud Analytic Services(CAS) ®, SAS Viya ® of all which are by SAS Institute Inc. of Cary, N.C.

Machine-learning models can be constructed through an at least partiallyautomated (e.g., with little or no human involvement) process calledtraining. During training, input data can be iteratively supplied to amachine-learning model to enable the machine-learning model to identifypatterns related to the input data or to identify relationships betweenthe input data and output data. With training, the machine-learningmodel can be transformed from an untrained state to a trained state.Input data can be split into one or more training sets and one or morevalidation sets, and the training process may be repeated multipletimes. The splitting may follow a k-fold cross-validation rule, aleave-one-out-rule, a leave-p-out rule, or a holdout rule. An overviewof training and using a machine-learning model is described below withrespect to the flow chart of FIG. 11A.

In block 1104, training data is received. In some examples, the trainingdata is received from a remote database or a local database, constructedfrom various subsets of data, or input by a user. The training data canbe used in its raw form for training a machine-learning model orpre-processed into another form, which can then be used for training themachine-learning model. For example, the raw form of the training datacan be smoothed, truncated, aggregated, clustered, or otherwisemanipulated into another form, which can then be used for training themachine-learning model.

In block 1106, a machine-learning model is trained using the trainingdata. The machine-learning model can be trained in a supervised,unsupervised, or semi-supervised manner In supervised training, eachinput in the training data is correlated to a desired output. Thisdesired output may be a scalar, a vector, or a different type of datastructure such as text or an image. This may enable the machine-learningmodel to learn a mapping between the inputs and desired outputs. Inunsupervised training, the training data includes inputs, but notdesired outputs, so that the machine-learning model has to findstructure in the inputs on its own. In semi-supervised training, onlysome of the inputs in the training data are correlated to desiredoutputs.

In block 1108, the machine-learning model is evaluated. For example, anevaluation dataset can be obtained, for example, via user input or froma database. The evaluation dataset can include inputs correlated todesired outputs. The inputs can be provided to the machine-learningmodel and the outputs from the machine-learning model can be compared tothe desired outputs. If the outputs from the machine-learning modelclosely correspond with the desired outputs, the machine-learning modelmay have a high degree of accuracy. For example, if 90% or more of theoutputs from the machine-learning model are the same as the desiredoutputs in the evaluation dataset, the machine-learning model may have ahigh degree of accuracy. Otherwise, the machine-learning model may havea low degree of accuracy. The 90% number is an example only. A realisticand desirable accuracy percentage is dependent on the problem and thedata.

In some examples, if the machine-learning model has an inadequate degreeof accuracy for a particular task, the process can return to block 1106,where the machine-learning model can be further trained using additionaltraining data or otherwise modified to improve accuracy. If themachine-learning model has an adequate degree of accuracy for theparticular task, the process can continue to block 1110.

In block 1110, new data is received. In some examples, the new data isreceived from a remote database or a local database, constructed fromvarious subsets of data, or input by a user. The new data may be unknownto the machine-learning model. For example, the machine-learning modelmay not have previously processed or analyzed the new data.

In block 1112, the trained machine-learning model is used to analyze thenew data and provide a result. For example, the new data can be providedas input to the trained machine-learning model. The trainedmachine-learning model can analyze the new data and provide a resultthat includes a classification of the new data into a particular class,a clustering of the new data into a particular group, a prediction basedon the new data, or any combination of these.

In block 1114, the result is post-processed. For example, the result canbe added to, multiplied with, or otherwise combined with other data aspart of a job. As another example, the result can be transformed from afirst format, such as a time series format, into another format, such asa count series format. Any number and combination of operations can beperformed on the result during post-processing.

A more specific example of a machine-learning model is the neuralnetwork 1150 shown in FIG. 11B. The neural network 1150 is representedas multiple layers of interconnected neurons, such as neuron 1158, thatcan exchange data between one another. The layers include an input layer1152 for receiving input data, a hidden layer 1154, and an output layer1156 for providing a result. The hidden layer 1154 is referred to ashidden because it may not be directly observable or have its inputdirectly accessible during the normal functioning of the neural network1150. Although the neural network 1150 is shown as having a specificnumber of layers and neurons for exemplary purposes, the neural network1150 can have any number and combination of layers, and each layer canhave any number and combination of neurons.

The neurons and connections between the neurons can have numericweights, which can be tuned during training. For example, training datacan be provided to the input layer 1152 of the neural network 1150, andthe neural network 1150 can use the training data to tune one or morenumeric weights of the neural network 1150. In some examples, the neuralnetwork 1150 can be trained using backpropagation. Backpropagation caninclude determining a gradient of a particular numeric weight based on adifference between an actual output of the neural network 1150 and adesired output of the neural network 1150. Based on the gradient, one ormore numeric weights of the neural network 1150 can be updated to reducethe difference, thereby increasing the accuracy of the neural network1150. This process can be repeated multiple times to train the neuralnetwork 1150. For example, this process can be repeated hundreds orthousands of times to train the neural network 1150.

In some examples, the neural network 1150 is a feed-forward neuralnetwork. In a feed-forward neural network, every neuron only propagatesan output value to a subsequent layer of the neural network 1150. Forexample, data may only move one direction (forward) from one neuron tothe next neuron in a feed-forward neural network.

In other examples, the neural network 1150 is a recurrent neuralnetwork. A recurrent neural network can include one or more feedbackloops, allowing data to propagate in both forward and backward throughthe neural network 1150. This can allow for information to persistwithin the recurrent neural network. For example, a recurrent neuralnetwork can determine an output based at least partially on informationthat the recurrent neural network has seen before, giving the recurrentneural network the ability to use previous input to inform the output.

In some examples, the neural network 1150 operates by receiving a vectorof numbers from one layer; transforming the vector of numbers into a newvector of numbers using a matrix of numeric weights, a nonlinearity, orboth; and providing the new vector of numbers to a subsequent layer ofthe neural network 1150. Each subsequent layer of the neural network1150 can repeat this process until the neural network 1150 outputs afinal result at the output layer 1156. For example, the neural network1150 can receive a vector of numbers as an input at the input layer1152. The neural network 1150 can multiply the vector of numbers by amatrix of numeric weights to determine a weighted vector. The matrix ofnumeric weights can be tuned during the training of the neural network1150. The neural network 1150 can transform the weighted vector using anonlinearity, such as a sigmoid tangent or the hyperbolic tangent. Insome examples, the nonlinearity can include a rectified linear unit,which can be expressed using the following equation:

y=max(x, 0)

where y is the output and x is an input value from the weighted vector.The transformed output can be supplied to a subsequent layer, such asthe hidden layer 1154, of the neural network 1150. The subsequent layerof the neural network 1150 can receive the transformed output, multiplythe transformed output by a matrix of numeric weights and anonlinearity, and provide the result to yet another layer of the neuralnetwork 1150. This process continues until the neural network 1150outputs a final result at the output layer 1156.

Other examples of the present disclosure may include any number andcombination of machine-learning models having any number and combinationof characteristics. The machine-learning model(s) can be trained in asupervised, semi-supervised, or unsupervised manner, or any combinationof these. The machine-learning model(s) can be implemented using asingle computing device or multiple computing devices, such as thecommunications grid computing system 400 discussed above.

Implementing some examples of the present disclosure at least in part byusing machine-learning models can reduce the total number of processingiterations, time, memory, electrical power, or any combination of theseconsumed by a computing device when analyzing data. For example, aneural network may more readily identify patterns in data than otherapproaches. This may enable the neural network to analyze the data usingfewer processing cycles and less memory than other approaches, whileobtaining a similar or greater level of accuracy.

According to embodiments discussed herein, the above-described computingdevices and systems may be utilized to summarize data visualizations,also referred to as images (e.g., graph images). The summaries of datavisualizations may be used to clearly communicate relevant parts of datavisualizations in an efficient and effective manner, resulting in acomputing device and/or system with exclusive and advantageouscapabilities. For example, generating a textual summary of a datavisualization may enable information contained in the data visualizationto be communicated to a visually impaired person, such as via a brailleterminal. In another example, a summary of a cardiogram may includenatural-language text that indicates whether or not any patternsassociated with an irregular heartbeat were detected in the cardio gram.

In some embodiments, the above-described computing devices and systemsmay implement a personalized graph summarizer to generate summaries ofdata visualizations. In various embodiments, the personalized graphsummarizer may analyze a data visualization to detect predefinedpatterns within the data visualization, and produce a textual summary ofthe data visualization based on pre-defined patterns detected within thedata visualization. In one or more embodiments, the personalized graphsummarizer may be able to learn additional types of data visualizationsand/or patterns to detect therein. For example, a personalized graphsummarizer may learn to identify and summarize a spectrogram. In someembodiments, the personalized graph summarizer may be able to generateand/or tailor summaries of data visualizations based on userpreferences. In some such embodiments, the personalized graph summarizermay learn user preferences based on interactions of the user with thepersonalized graph summarizer. For instance, the personalized graphsummarizer may assign or alter priority levels associated with one ormore sentences in a summary based on input received from a user.

In various embodiments, the personalized graph summarizer may includethe ability to extract context from a data visualization. In varioussuch embodiments, the personalized graph summarizer may use contextextracted from a data visualization to improve clarity and readabilityof a natural-language textual summary for the data visualization. Forexample, extracted context may alter a sentence in a summary to read “Aspike in dollar amount between Aug. 6, 2015 and Aug. 15, 2015.” insteadof “A spike in values.” based on context extracted from the datavisualization being summarized These and other features of thepersonalized graph summarizer may enable a computing device and/orsystem implementing the personalized graph summarizer to realize uniqueand advantageous functionalities, resulting in an improved computer.

FIG. 12A illustrates an example of an operating environment 1200 thatmay be representative of various embodiments. In operating environment1200, system 1205 may receive or identify input 1201 and generate apersonalized summary 1204 based on input 1201. For instance, system 1205may receive an image file that includes a graph image as input 1201,analyze the graph image to detect one or more pre-defined patterns inthe graph image, and generate personalized summary 1204 based on the oneor more pre-defined patterns detected in the graph image. In someembodiments, these operations may be performed in real-time or nearreal-time by system 1205. Further, operating environment 1200 mayinclude a number of systems, components, devices, and so forth toperform these operations; however, embodiments are not limited in thismanner In some embodiments, operating environment 1200 may include moreor less systems, components, and devices, for example. In variousembodiments, operating environment 1200 may be implemented via one ormore devices of FIG. 2. Embodiments are not limited in this context.

In the illustrated embodiments, system 1205 includes a number ofcomponents to generate personalized summary 1204 based on input 1201,including, but not limited to, personalized graph summarizer (PGS) 1202,memory 1210, storage 1215, processing circuitry 1220, and one or moreinterfaces 1225. In various embodiments, system 1205 may be coupled withone or more other systems, components, devices, networks and so forth,such as via interfaces 1225. In various such embodiments, the one ormore other systems, components, devices, networks and so forth mayperform one or more functions described herein. For instance, a servermay perform one or more operations of PGS 1202.

Storage 1122 may be any type of storage, including, but not limited to,magnetic storage and optical storage, for example. In some instances,storage 1122 may be part of one or more of the storage systems 1130-1through 1130-4 and may be a DAS, NAS, or SAN. The storage 1122 may storeinformation and data for system 1205, such as information for processingby the by the system 1205. In embodiments, the storage 1122 may storeinformation, data, one or more instructions, code, and so forth for PGS1202.

The memory 1124 of system 1205 can be implemented using anymachine-readable or computer-readable media capable of storing data,including both volatile and non-volatile memory. In some embodiments,the machine-readable or computer-readable medium may include anon-transitory medium. The embodiments are not limited in this context.The memory 1124 can store data momentarily, temporarily, or permanently.The memory 1124 stores instructions and data for system 1205, which maybe processed by processing circuitry 1126. For example, the memory 1124may also store temporary variables or other intermediate informationwhile the processing circuitry 1126 is executing instructions. Thememory 1124 is not limited to storing the above discussed data; thememory 1124 may store any type of data. In various embodiments, one ormore portions of PGS 1202 may be stored in memory 1210 and/or storage1215. In various such embodiments, PGS 1202 may reside in storage 1215and/or memory 1210. In some embodiments, memory 1210 may include randomaccess memory (RAM).

In embodiments, the system 1205 may include processing circuitry 1126which may include one or more of any type of computational element, suchas but not limited to, a microprocessor, a processor, central processingunit, digital signal processing unit, dual core processor, mobile deviceprocessor, desktop processor, single core processor, a system-on-chip(SoC) device, complex instruction set computing (CISC) microprocessor, areduced instruction set (RISC) microprocessor, a very long instructionword (VLIW) microprocessor, or any other type of processing circuitry,processor or processing circuit on a single chip or integrated circuit.The processing circuitry 1126 may be connected to and communicate withthe other elements of the system 1205 including the modeling system1210, the storage 1122, the memory 1124, and the one or more interfaces1220. In one or more embodiments data associated with PGS 1202 may bemoved from storage 1215 to memory 1210 to provide processing circuitry1220 access thereto. For instance, processing circuitry 1220 may performcomputations with and/or manipulate data associated with PGS 1202 thatis stored in memory 1210.

The system 1205 may also include one or more interfaces 1220 which mayenable the system to communicate over the network environment 135. Insome embodiments, the interfaces 1220 can be a network interface, auniversal serial bus interface (USB), a Firewire interface, a SmallComputer System Interface (SCSI), a parallel port interface, a serialport interface, a network adapter, a radio, or any other device toenable the system 1205 to exchange information. In various embodiments,interfaces 1225 may include one or more input/output (I/O) devices, suchas a display, a touch screen, a monitor, a keyboard, a mouse, a brailleterminal, or any other devices capable of presenting data to a user orreceiving data from a user. In various such embodiments, one or more I/Odevices may be utilized to receive input 1201 or present personalizedsummary 1204.

In various embodiments described herein, PGS 1202 may enable system 1205to provide a tool that enables users to automatically summarize datavisualizations (i.e., images of data visualizations). In someembodiments, PGS 1202 may include one or more features to enablepersonalization of summaries. In some such embodiments, PGS 1202 mayenable a user to create new personalized patterns to be searched forwithin input 1201 and/or tailor text of personalized summary 1204. Inone or more embodiments, PGS 1202 may include a flexible environmentthat enables users to interact with it, such as via one or more ofinterfaces 1225. For instance, PGS 1202 may include a graphical userinterface (GUI) presented on a display. In various such instances, theGUI 1201 may provide an interface through which one or more of input1201 may be received or personalized summary 1204 may be provided to auser. Personalization may increase the applicability of PGS 1202,enabling a user to improve productivity though utilization of PGS 1202.

FIG. 12B illustrates an example of a processing flow 1250 of PGS 1202that may be representative of various embodiments. In processing flow1250, PGS 1202 may include visual pattern detector 1251, personalizedpattern creator 1252, summary generator 1254, summary personalizer 1256,and context extractor 1258. In some embodiments, visual patter detector1251 may analyze input 1201 by detecting pre-defined patterns. Invarious embodiments, personalized pattern creator 1252 may enable a userto create or modify one or more of the pre-defined patterns searched forby visual pattern detector 1251. In one or more embodiments, summarygenerator 1254 may generate and arrange one or more text templates basedon identification of patterns. In some embodiments, context extractor1258 may recover data from input 1201, such as via optical characterrecognition (OCR). In various embodiments, summary personalizer 1256 maylearn preferences of a user. In one or more embodiments, summarypersonalizer 1256 may determine user preferences via revisions made to asummary by the user. Embodiments are not limited in this context.

As described above and as will be described in more detail below, suchas with respect to FIGS. 13A-18, the components of PGS 1202 may operateto generate personalized summary 1204 based on input 1201. Inembodiments, these operations may include one or more of the following.

In one or more embodiments, PGS 1202 may identify a data visualizationcomprising a graph image. In one or more such embodiments, the datavisualization may include an image or image file. In variousembodiments, PGS 1202 may determine a set of graph-type correlationscores for the graph images. In some embodiments, the set of graph-typecorrelation scores may include a graph-type correlation score for eachgraph type of a plurality of graph types. In various embodiments, eachgraph-type correlation score may be based on a comparison of at least aportion of the graph image with one or more graph-type models associatedwith each graph type of the plurality of graph types. In one or moreembodiments, PGS 1202 may evaluate the set of graph-type correlationscores to identify a graph type of the graph image. In variousembodiments PGS 1202 may retrieve a set of patterns based on the graphtype of the graph image. In some embodiments, each pattern in the set ofpatterns may include one or more pattern examples.

In some embodiments, PGS 1202 may determine a set of region of interest(ROI) correlation scores for the graph image based on matching the oneor more pattern examples of each pattern in the set of patterns with atleast a portion of the graph image. In various embodiments, the set ofROI correlation scores may include at least one ROI correlation scorefor each pattern in the set of patterns. In embodiments, PGS 1202 mayevaluate the set of ROI correlation scores to identify one or morecandidate ROIs of the graph image. In one or more embodiments, each ofthe one or more candidate ROIs may include a portion of the graph image.In some embodiments, PGS 1202 may overlay the pattern example of thegraph image in a plurality of positions to match a pattern example of apattern in the set of patterns with at least a portion of the graphimage. In some such embodiments, PGS 1202 may use a sliding window tomatch the pattern example of the pattern in the set of patterns with atleast a portion of the graph image. In various embodiments, PGS 1202 maycompute an ROI correlation score in the set of ROI correlation scoresfor each of the plurality of positions.

In one or more embodiments, PGS 1202 may retrieve a set of patternmodels based on the set of candidate ROIs of the graph image. In someembodiments, each candidate ROI in the set of candidate ROIs may beassociated with one pattern model in the set of patterns. In variousembodiments, each pattern model in the set of pattern models may beassociated with one pattern in the set of patterns. In some embodiments,PGS 1202 may compare each candidate ROI in the set of candidate ROIs toan associated pattern model in the set of pattern models to determine aset of pattern model correlation scores. In one or more embodiments, theset of pattern model correlation scores may include a pattern modelcorrelation score for each candidate ROI of the one or more candidateROIs. In some embodiments, each pattern model correlation score mayindicate a likelihood of a respective candidate ROI of the one or morecandidate ROIs including an associated pattern.

In various embodiments, PGS 1202 may identify one or more detectedpatterns based on the set of pattern model correlation scores. Invarious such embodiments, PGS 1202 may retrieve one or more texttemplates based on the one or more detected patterns. In someembodiments, the one or more text templates may include at least oneportion of text associated with each detected pattern of the one or moredetected patterns. In various embodiments, each text template of the oneor more text templates may be associated with a priority level. In oneor more embodiments, PGS 1202 may detect a portion of the graph imagewith contextual information. In one or more such embodiments, PGS 1202may extract a textual element from the portion of the graph image withcontextual information. In some embodiments, PGS 1202 may insert atleast a portion of the contextual information into at least one texttemplate of the one or more text templates to generate the textualdescription of the graph image. In various embodiments, PGS 1202 mayidentify a component of the graph image based on the graph type. Invarious such embodiments, PGS 1202 may determine contextual informationis absent from the portion of the graph image with potential contextualinformation based on the component of the graph image identified basedon the graph type. In one or more embodiments, PGS 1202 may detect aportion of the graph image with contextual information.

In some embodiments, PGS 1202 may arrange the one or more text templatesin an order based on the priority level associated with each texttemplate to generate a textual description of the graph image. In one ormore embodiments, the summary of the graph image may include the graphimage and the textual description of the graph image. In variousembodiments, PGS 1202 may present the one or more text templatesarranged based on the priority level associated with each text templatevia a user interface. In various such embodiments, PGS 1202 may arrangethe one or more text templates in an updated order based on inputreceived via the user interface. In some such embodiments, PGS 1202 mayalter a priority level of at least one of the one or more text templatesbased on the updated order. In one or more embodiments PGS 1202 mayalter the priority level of text template based on input received via auser interface. In some embodiments, PGS 1202 may generate the textualdescription based on the priority level associated with each texttemplate. In various embodiments, the priority level of the at least oneof the one or more text templates altered based on the updated order. Inone or more embodiments, PGS 1202 may produce a summary of the graphimage.

In various embodiments, PGS 1202 may receive an additional patternexample. In various such embodiments, PGS 1202 may update a patternmodel in the set of pattern models based on the additional patternexample. In some embodiments, at least one pattern in the set of pattersmay comprise a personalized pattern. In one or more embodiments, PGS1202 may create the personalized pattern based on one or more examplegraph images and one or more pattern examples identified in the examplegraph images based on input received via a user interface. Inembodiments, PGS 1202 may associate one or more of a priority level, atext template, or a graph type with the personalized patter based oninput received via the user interface. The embodiments are not limitedin the context of these operations. It will be appreciated that theseoperations are not limiting, and different or additional operationsmaybe performed by PGS 1202 in generating personalized summary 1204 frominput 1201 without departing from the scope of this disclosure.

FIGS. 13A-13H illustrate operations of that may be performed by thepersonalized pattern creator (PPC) 1252. In various embodiments, theoperations may create a personalized pattern that PGS 1201 can identifyand summarize. In various such embodiments, the operations may includeone or more of pre-processing input, extracting features, updating oneor more collections, training classifiers, or updating a patterndictionary. For instance, example graph images, each comprising a commonpersonalized pattern to be created or updated, may be received as inputand pre-processed. In such instances, after pre-processing, features maybe extracted from the example graph images. In some embodiments, theextracted features may include one or more mathematical representationsof the example graph images. In one or more embodiments, the examplegraph images may then be used to update or create a new set of examplegraph images associated with the personalized pattern. In one or moresuch embodiments, a classifier may then be trained on the updated or newset of example graph images. In various embodiments, the classifier maythen be used to update or create a pattern model in a pattern modeldictionary. In various such embodiments, the pattern model may comprisethe classifier. In some embodiments, once the updated or created patternmodel is stored in the pattern model dictionary, PGS 1202 may be able toidentify and summarize it. Embodiments are not limited in this context.

Referring to FIG. 13A, PPC 1252 may identify or receive input 1301 forcreating a personalized pattern to add to the pre-defined patterns thatPGS 1202 may detect and summarize. In some embodiments, for instance, tocreate a personalized pattern, input 1301 may include one or more ofexample graph images, identification of ROIs, an insight message (e.g.,“higher outlier (spike)”), a text template (e.g., A steading increase indata points with a higher outlier or spike“), a graph type (e.g., linechart), or a priority level (e.g., high). In some such embodiments, PPC1252 may take this information and learn the personalized pattern andsave it to a dictionary of pre-defined patterns or pattern models. Inone or more embodiments, PPC 1252 may learn a pattern by generating apattern model based on characteristics of the pattern examples. In oneor more such embodiments, PPC 1252 may utilize a machine learningalgorithm, such as in a Deep Neural Network (DNN), to learn a pattern.In various embodiments, the example images may also be used to improvegraph type identification. For example, the additional graph-typeexamples provide more data to train a graph-type classifier, and themore data available for training can result in an improved classifier.

In various embodiments, PPC 1252 may include pre-processor 1302, featureextractor 1304, collection updater 1306, classifier trainer 1308, andpattern dictionary updater 1310. Generally, operation of PPC 1252 mayproceed as follows. In one or more embodiments, pre-processor 1302 maygenerate one or more regions of interest (ROIs) associated with input1301. In one or more such embodiments, the ROIs may be generated basedon highlighted regions of images in input 1301. For instance, an ROI mayinclude a spike in values highlighted in a graph image provided as input1301. In some embodiments, pre-processor 1302 may pre-process input1301. In some such embodiments, pre-processing may include one or moreof denoising, resizing, or adjusting orientation of images. For example,images in input 1301 may be resized to a first standard size while ROIsare resized to a second standard size. In various embodiments, featureextractor 1304 may extract characteristic features associated with input1301 (e.g., from the one or more ROIs). In various such embodiments,this may include one or more of raw pixels or a histogram of orientedgradients. In one or more embodiments, collection updater 1306 mayupdate one or more of a graph-type examples collection or a patternexamples collection. For instance, images in input 1301 may be added tothe graph-type examples collections and ROIs associated with input 1301may be added to the pattern examples collection. In various embodiments,classifier trainer 1308 may generate one or more of a graph-typeclassifier or a pattern model classifier. In various such embodiments,the graph-type classifier and/or pattern model classifier may begenerated based on one or more of feature characteristics,characteristics mapping, graph-type examples collection, or patternexamples collection. In some embodiments, pattern dictionary updater1310 may update one or more of a graph-type models collection or apattern models collection based on the classifiers generated byclassifier trainer 1308. In one or more embodiments, the pattern modelscollection may include a pattern model for each pre-defined pattern thatPGS 1202 may detect. In some embodiments, the pattern models collectionmay include a pattern model for one or more personalized patterns.

Referring to FIG. 13B, in some embodiments, input 1301 may includeexample images 1320, graph-type 1326, insight message 1328, texttemplate(s) 1330, and priority level 1332. Further, example images 1320may include one or more original images 1322 and one or more highlightedimages 1324. In various embodiments described herein, original images1322 may include a data visualization. As shown in FIG. 13C, originalimage 1322 may include a clean graph image while highlighted image 1324includes the graph image with a bounding box around ROI 1334. It will beappreciated that a user may be prompted for one or more components ofinput 1301 at different times. For instance, PPC 1252 may provide aninterface that allows a user to highlight ROI 1334 on original image1322 to create highlighted image 1324. In some embodiments, input 1301may include different or additional components.

Referring to FIG. 13D, in various embodiments, pre-processor 1302 maygenerate one or more ROIs (e.g., ROI 1334) associated with input 1301.For instance, an ROI 1334 may include a spike in values of a graph imageprovided as input 1301 (see e.g., FIG. 13C). In various embodiments,pre-processor 1302 may extract ROI 1334 from highlighted image 1324. Insome embodiments, ROI 1334 may be extracted from highlighted image 1324by removing all portions of original image 1322 except ROI 1334. Inembodiments that multiple ROIs are included in highlighted image 1324,pre-processor 1302 may extract each ROI as an independent object. Invarious embodiments, pre-processor 1302 may pre-process input 1301. Invarious such embodiments, pre-processing may include one or more ofdenoising, resizing, or adjusting orientation of images. In one or moreembodiments, pre-processor 1302 may resize one of more of example images1320 or ROI 1334 to one or more standard sizes. For example, originalimage(s) 1322 may be resized to a first standard size while ROIs (e.g.,ROI 1334) are resized to a second standard size.

Moving now to FIG. 13E, in various embodiments, feature extractor 1304may extract characteristic features associated with input 1301, such asfrom one or more of original image 1322 or ROI 1334. In various suchembodiments, this may include one or more of raw pixels or a histogramof oriented gradients. In some embodiments, features may be mathematicalrepresentations of images. In one or more embodiments, these featuresmay be used to describe characteristics of a pattern. In someembodiments, ROI 1334 may be broken down into features 1336. In somesuch embodiments, one or more of feature characteristics 1338 orcharacteristics mapping 1340 may be generated based on features 1336. Invarious embodiments, PGS 1202 may utilize any combination of featuresets having rotation and scale invariant descriptors (e.g., raw pixels).In various such embodiments, rotation and scale invariant features(e.g., densely sampled histogram of oriented gradients (HOG)) may enablePGS 1202 to handle rotated graphs as well as graphs at different scales.

Referring now to FIG. 13F, collection updater 1306 may use originalimage(s) 1322 to create, update, and/or maintain graph-type examplescollection 1342 and ROI 1334 to create, update, and/or maintain patternexamples collection 1344. In various embodiments, original image 1322may be stored under a graph-type identified in input 1301 (e.g.,graph-type 1326) as a graph-type example (e.g., graph-type example(s)1347-1, 1347-2, 1347-n). In some embodiments, original image 1322 maycause a new graph type to be created in graph-type examples collection1342. As shown in FIG. 13F, graph-type examples collection 1342 mayinclude any number of graph types 1346-1, 1346-2, 1346-n under whichoriginal images 1322 may be stored as or added to as graph type examples(e.g., graph-type example(s) 1347-1, 1347-2, 1347-n). Further, eachgraph-type may include any number of graph type examples.

Similarly, pattern examples collection 1344 may be stored in patternexamples collection 1344 as a pattern example in a set of one or morepattern examples (e.g., pattern example(s) 1350-1, 1350-2, 1350-n,1354-1, 1354-2, 1354-n, 1356-1, 1356-2, 1356-n). In one or moreembodiments, ROI 1334 is stored in a set of pattern examples under oneor more classifications or associations. For instance, each set ofpattern examples may be associated with a specific pattern (e.g.,pattern 1348-1, 1348-2, 1348-n, 1352-1, 1352-2, 1352-n, 1356-1, 1356-2,or 1356-n) and a specific graph type (e.g., graph type 1346-1, 1346-2,or 1346-n). In various embodiments, collection updater 1306 create oneor more of a new graph type, pattern, or pattern example to accommodateROI 1334 in pattern examples collection 1344.

In FIG. 13G, classifier trainer 1308 may use one or more of featurecharacteristics 1338, characteristics mapping 1340, graph-type examplescollection 1342, or pattern examples collection generate or train one ormore of graph-type classifier 1366 or a pattern model classifier 1368.In various such embodiments, the graph-type classifier 1366 and/orpattern model classifier 1368 may be generated based on one or more offeature characteristics, characteristics mapping, graph-type examplescollection, or pattern examples collection. In some embodiments,graph-type classifier 1366 may be generated based on or trained with oneor more example images (e.g., graph-type examples 1347-1, 1347-2,1347-n) received during operation of PPC 1252 and/or a pre-existingcollection of graph type images. For instance, PGS 1202 may comepreloaded with the pre-existing collection of graph type images. Invarious embodiments, pattern model classifiers 1368 may be generated ina similar manner to that of graph-type classifier 1366, except based ondifferent example images (e.g., pattern examples 1350-1 through 1358-n).

In one or more embodiments, classifier trainer 1308 may utilize machinelearning algorithms to generate or train one or more of graph-typeclassifier 1366 and pattern model classifier 1368. For example, amachine learning algorithm may take pattern examples associated with aspecific pattern (e.g., pattern examples 1350-1 of pattern 1348-1) asinput and generate a corresponding pattern model classifier as output.In one or more such embodiments, these machine learning algorithms mayutilize one or more of a Support Vector Machine (SVM), a Deep NeuralNetwork (DNN), Random Forest Trees, Naïve Baes, Boosting, and othermachine learning algorithms or techniques. In various embodiments, PGS1202 may initially support a set of initial graph types, such as lineargraphs and bar graphs. However, in some embodiments, the flexible andmodular design of PGS 1202 may support learning further graph types.Further, in one or more embodiments, updating graph-type examplescollection 1342 and pattern examples collection 1344 with additionalexamples may improve the accuracy of the generated classifiers (e.g.,graph-type classifier 1366 and/or pattern model classifier 1368) byenabling a larger training set to be input to the machine learningalgorithm that generates the classifiers.

Moving to FIG. 13H, in some embodiments, pattern dictionary updater 1310may create, update, and/or maintain one or more portions of a modeldictionary 1370. In various embodiments, model dictionary 1370 mayinclude one or more of a graph-type models collection 1380 or a patternmodels collection 1382 that are created, updated, and/or maintainedbased on the classifiers generated by classifier trainer 1308. In someembodiments, a graph-type model may comprise an associated graph-typeclassifier. For instance, graph-type model 1372-1 for graph type 1346-1may include a graph-type classifier trained on graph-type examples1347-1. Similarly, in various embodiments, a pattern model may comprisean associated pattern model classifier. For example, pattern model1376-1 for pattern 1352-1 may include a pattern model classifier trainedon pattern examples 1354-1. In one or more embodiments, the patternmodels collection 1382 may include a pattern model for one or more ofthe pre-defined patterns that PGS 1202 may detect (e.g., pattern 1348-1,1348-2, 1348-n, 1352-1, 1352-2, 1352-n, 1356-1, 1356-2, 1356-n). In someembodiments, the patterns may be grouped by graph type (e.g., graph type1346-1, 1346-2, 1346-n). In various embodiments, the graph-type modelscollection 1380 may include a graph-type model (e.g., graph-type model1372-1, 1372-2, 1372-n) for each graph type that PGS 1202 can identify.In one or more embodiments, the models may be used to generate anumerical likelihood that an associated pattern or graph-type is presentin an image provided to PGS 1202 for generation of a personalizedsummary (e.g., personalized summary 1204).

FIGS. 14A-14G illustrate operations of that may be performed by thevisual pattern detector (VPD) 1251. In various embodiments, theoperations may include of one or more of graph-type identification,candidate ROI detection, pattern detections, and quality analysis.Referring to FIG. 14A, VPD 1251 may identify or receive input 1201 tosearch for one or more patterns to enable generation of personalizedsummary 1204. In some embodiments, such as the embodiment of FIG. 14B,input 1201 may include an image 1410 (i.e., data visualization) that VPD1251 searches for one or more pre-defined patterns. In the illustratedembodiments, image 1410 includes a graph image. Embodiments are notlimited in this context.

In one or more embodiments described herein, the detection of patternsin image 1410 by VPD 1251 may proceed as follows. Classify the graphtype in image 1410 based on one or more graph-type models in graph-typemodels collection 1380 (e.g., graph-type models 1372-1, 1372-2, 1372-n).Retrieve a list of patterns for the graph type of the image 1410. Forinstance, if the graph type of image 1410 is identified as graph type1346-1, the list of patterns may include patterns 1348-1, 1348-2,1348-n. For each pattern associated with the identified graph type ofimage 1410, the associated set of pattern example(s) may be retrievedfrom pattern examples collection 1344 and for each pattern example, adetermined number of ROIs in image 1410 that have high correlations witha respective pattern example may be selected. For instance, the fiveROIs in image 1410 that correlate with each respective pattern examplethe closest may be example may be selected. For each pattern example,the selected ROIs may then be run against the pattern model associatedwith the same pattern as the pattern examples to determine a likelihoodof detecting a respective pattern in each of the respective selectedROIs. Finally, the likelihoods are evaluated to identify one or moredetected patters. In various embodiments, the detected patterns may bepassed to summary generator 1254 for generation of personalized summary1204 based on the detected patterns.

Referring to FIG. 14C, graph-type identifier 1402 may be responsible foridentifying and/or associating a graph-type with image 1410. In one ormore embodiments, associating a graph-type with image 1410 may enableone or more patterns specific to that graph type to be retrieved, suchas from one or more of model dictionary 1370, graph-type examplescollection 1342, or pattern examples collection 1344. In variousembodiments, graph type identifier may include a graph-type correlationscore assessor (GTCSA) 1412. In some embodiments, GTCSA 1412 may computea graph-type correlation score for one or more of graph types 1346-1,1346-2, 1346-n (e.g., graph-type correlation scores 1422-1, 1422-2,1422-n) based on image 1410. In various embodiments, GTCSA 1412 mayutilize one or more graph-type classifiers generated by classifiertrainer 1308 (e.g., graph-type classifier 1366) to compute thegraph-type correlation scores. For instance, GTCSA 1412 may run image1410 against each graph-type model in graph-type models collection 1412to generate a graph-type correlation score associated with image 1410for each respective graph type. In such instances, a graph-typeclassifier trained on examples of a respective graph type may be used togenerate the graph-type correlation score for the respective graph type.In one or more embodiments, graph-type identifier 1402 may include agraph-type correlation score evaluator (GTCSE) 1424. In one or more suchembodiments, GTCSE 1424 may associate image 1410 with an identifiedgraph-type 1426 based on a comparison of the graph-type correlationscores.

Proceeding to FIG. 14D, the identified graph-type 1426 may be passed tocandidate ROI detector 1404. In one or more embodiments, candidate ROIdetector 1404 may identify candidate ROIs by comparing example images ofa pattern with image 1410. In one or more such embodiments, this mayinclude matching and/or computation of a normalized correlation scorebetween image 1410 and a respective example image. The normalizedcorrelation scores may indicate a similarity between the example imagesof a pattern and image 1410. In some embodiments, a matchlmage actionmay be utilized for this purpose.

In some embodiments, matching that includes a sliding window method maybe utilized to compute correlation scores for the identification ofcandidate ROIs 1432. For instance, portions or patches of an exampleimage (e.g., pattern example 1350-2) may be overlaid on image 1410 in aplurality of positions, such as by being slid horizontally andvertically over image 1410. In such instances, a correlation score maybe computed for each patch position (i.e., each of the plurality ofpositions), and candidate ROIs 1432 may be identified as patch positionswith an associated correlation score that satisfies one or morecriteria. In various embodiments, the sliding window method may beutilized when image 1410 is larger than the example images. In variousembodiments, the correlation scores may be compared to a threshold todetermine candidate ROIs 1432. In other embodiments, the correlationscores may be compared to each other to determine candidate ROIs 1432.For example, the top five correlation scores for each example image maybe selected as candidate ROIs 1432.

In one or more embodiments, candidate ROI detector 1404 may retrieve oneor more pattern examples associated with the identified graph-type 1426from pattern examples collection 1344 (e.g., pattern example(s) 1450-1,1450-2, 1450-n. For instance, if identified graph-type 1426 correspondsto graph type 1346-2, pattern examples 1354-1, 1354-2, 1354-n may beretrieved from pattern examples collection 1344.

In some embodiments, candidate ROI detector 1404 may include ROIconfidence score assessor (RCSA) 1428 and ROI confidence score evaluator(RCSE) 1430. In various embodiments, RCSA 1428 may compute one or moreconfidence scores associated with each of pattern examples 1450-1,1450-2, 1450-n. In various such embodiments, RCSE 1430 may determine oneor more candidate ROIs 1432 based on the confidence scores. For example,for each pattern associated with the identified graph type 1426 of image1410, the associated set of pattern example(s) may be retrieved frompattern examples collection 1344 and for each pattern example, adetermined number of ROIs in image 1410 that have high correlations witha respective pattern example may be selected as candidate ROIs 1432. Ina further example, the five ROIs in image 1410 that correlate with eachrespective pattern example the closest may be selected as candidate ROIs1432.

In one or more embodiments, pseudo code for operations performed bycandidate ROI detector 1404 may include one or more of:

1: listOfPatterns ← getPatterns(graph-type) 2: I ← input image 3: k ←number of best ROIs listOfROI ← [ ] 4: for each pattern p inlistOfPatterns do 5:  for each example image i of p do 6:   l ←detectCandidateROI(I, i, k) 7:   listOfROI.append(l) 8:  end for 9: endfor

Moving to FIG. 14E, the candidate ROIs 1432 may be passed to patterndetector 1406. In various embodiments, pattern detector 1406 mayretrieve one or more pattern models associated with the identifiedgraph-type 1426 and each of the patterns associated with the candidateROIs 1432 from pattern models collection 1382 (e.g., pattern models1448-1, 1448-2, 1448-n. In some embodiments, pattern detector 1406 mayinclude pattern model correlation score assessor (PMCSA) 1434. Invarious embodiments, PMCSA 1434 may compute one or more confidence orcorrelation scores associated with each of pattern models 1448-1,1448-2, 1448-n to determine one or more candidate pattern(s) 1436 inimage 1410. In one or more embodiments, PMSCA 1434 may utilize one ormore of pattern model classifiers 1368 to compute the correlation scoresassociated with each of the pattern models. For example, a pattern modelclassifier trained on examples of a respective pattern may be used togenerate the pattern model correlations score for the respectivepattern.

In FIG. 14F, one or more candidate patterns 1426 along with theirrespective pattern model correlation scores 1438 may be passed toquality analyzer 1408. In some embodiments, quality analyzer 1408 mayinclude a pattern model correlation score evaluator (PMCSE) 1440. Insome such embodiments, PMCSE 1440 may determine one or more detectedpatterns 1444 associated with image 1410 based on an evaluation of thepattern model correlation scores. For example, in FIG. 14G, candidatepattern 1436-1 may be associated with a pattern model correlation score1438-1 of 0.06, candidate pattern 1436-2 may be associated with apattern model correlation score 1438-2 of 0.03, and candidate pattern1436-n may be associated with a pattern model correlation score 1438-nof 0.8. In such examples, PMCSE 1440 may select candidate pattern 1426-nas a detected pattern 1444-1 in detected patterns 1444 in response todetermining candidate pattern 1436-n has a pattern model correlationscore that meets or exceeds a threshold. In other words, candidatepatterns that are not associated with a strong enough pattern modelcorrelation score may be removed prior to passing the detected patterns1444 to summary generator 1254. In various embodiments, instead of athreshold, a classifier (similar to graph-type classifier 1366 and/orpattern model classifier 1368) may be trained to determine whether acandidate pattern should be selected as a detected pattern. In varioussuch embodiments example data including a set of pattern modelcorrelation scores and an indication of whether the candidate patternassociated with each of the correlation scores was selected as adetected pattern may be used to train the classifier.

FIG. 15 illustrates an example of a processing flow 1500 of summarygenerator 1254 that may be representative of various embodiments. Inprocessing flow 1500, summary generator 1254 may include documentplanner 1502, sentence constructor 1504, and text generator 1506. Insome embodiments, summary generator 1254 may produce personalizedsummary 1204 based on detected patterns 1444. In some such embodiments,summary generator 1254 may generate personalized summary 1204 based oninformation received from one or more of context extractor 1258 andsummary personalizer 1256. In one or more embodiments, summary generator1254 may perform natural language processing. For instance, sentenceconstructor 1504 and context extractor 1258 may generate naturallanguage from context extracted from an example image. Embodiments arenot limited in this context.

As previously described, each pattern may be associated with an insightmessage and one or more text templates. In various embodiments, the oneor more text templates may be designed to express in natural languagethe insight included in the insight message. In various suchembodiments, this may be in combination with other closely relatedinsights. In some embodiments, summary generator 1254 may begin withdocument planner 1502. In one or more embodiments, document planner 1502may arrange the insight messages in a logical manner (e.g., based on ormore of a priority level or user preferences). Document planner 1502 maythen pass the arranged insight messages, for instance, as a group, tosentence constructor 1504.

In one or more embodiments, sentence constructor 1504 may match thearranged insight messages to one or more text templates. In variousembodiments, sentence constructor 1504 may insert one or more portionsof context identified by context extractor 1258 into one or more of thetext templates. For example, a sample text template may be “A steadyincrease in data points with a higher outlier or spike.” In suchexamples, ‘data points’ may not always be part of the text template, andinstead it may be replaced with relevant context identified by contextextractor 1258. Thus, with the appropriate context, the sample texttemplate may be modified to be “A steady increase in dollar amount overtime with a higher outlier or spike.”

In some embodiments, PGS 1202 may be designed to work with whateverinformation is available. Accordingly, a text template may remain as isif context is unavailable. However, with the availability of context,either from a user or extracted by context extractor 1258, a texttemplate may be converted into a more fine-grained text template basedon the context. In various embodiments, text generator 1506 may checkthe grammatical correctness of the text templates and adds any neededmarkup in order to produce personalized summary 1204. In one or moreembodiments, text generator 1506 may add one or more markups or make oneor more revisions based on input received via summary personalizer 1256.

FIG. 16 illustrates an example of a processing flow 1600 of contextextractor 1258 that may be representative of various embodiments. Inprocessing flow 1600, context extractor 1258 may include text detector1602, text recognizer 1604, and text validator 1606. In one or moreembodiments described herein, context extractor 1258 may detect aportion of input 1601 with contextual information. In one or more suchembodiments, context extractor 1258 may generate or extract context 1608(e.g., textual elements) from the portion of input 1601 with contextualinformation. In some embodiments, input 1601 be the same or similar toinput 1201. Thus, in the illustrated embodiments, input 1601 includesimage 1410 and identified graph-type 1426. In one or more embodimentscontext extractor 1258 may utilize identified graph-type 1426 to extractcontext 1608 from image 1410. In one or more such embodiments, know thegraph type may provide prior knowledge of the components of an image,such as the location of axes, and data labels. In various embodiments,context extractor 1258 may utilize optical character recognition (OCR)and/or computer vision. Embodiments are not limited in this context.

In one or more embodiments, context extraction may include multiplesteps. For instance, text detector 1602 may detect contextualinformation, such as textual elements in image 1410. In someembodiments, text recognizer 1604 may then determine whether thedetected textual elements include one or more of a title, a name, anaxis label, or a legend, and extract the detected textual elements. Invarious embodiments, text validator 1606 may be included in contextextractor 1258 to provide the ability to request verification from auser for the proper recognition of textual elements. In someembodiments, text validator 1606 may enable a user to modify theextracted textual elements to improve accuracy of context extractor1258.

As context extractor 1258 knows the graph type of image 1410, it alsohas prior knowledge of the graph, and may use this information torectify a text detection algorithm to avoid false positives. In someinstances, detected text blobs (the output of text detector 1602) maythen be feed to text recognizer 1604. In various embodiments, a textrecognition model may be created offline based on one or more known,supported, and non-cursive font types, such as Arial. After detection,recognition, extraction, and, if necessary, verification, contextextractor 1258 may parse the information extracted from image 1410 sothat PGS 1202 may consume it as context 1608. For instance, a data labelsuch as S10,000,000 may become “dollar amount”. Similarly, “Nov-16” and“Mar-17” may become “over time”.

In a further example, a graph image with revenue on the y-axis and yearon the x-axis may be received for summarization. In such examples,context extractor 1258 may identify that the values on the y-axis mayinclude a dollar sign (e.g., ‘S’) and the values on the x-axis may befour digit numbers (e.g., 2014, 2015, 2016). Based on this information,context extractor 1258 may determine that the y-axis represents sometype of resource information and the x-axis represents the year. Invarious embodiments, context extractor 1258 may identify a title of‘Revenue v. Year’ in the graph image. In various such embodiments, thecontext extractor 1258 may determine the type of monetary informationrepresented by the y-axis is revenue. In one or more embodiments, thisinformation may enable the summary to be detailed. For instance, ratherthan a summary that includes “a linear increase”, a detailed summarythat includes “a linear increase in revenue by year”. In anotherinstance, context extractor 1258 may identify axis labels, such as “homeprice” and “square foot”. In such other instances, the axis labels maybe used to contextually enrich the summary In various embodimentsdescribed herein, more contextual information extracted from the graphimage may result in a more detailed summary

FIG. 17 illustrates an example processing flow of a summary personalizer1256 that may be representative of various embodiments. In processingflow 1700, summary personalizer 1256 may include user interface 1702 andpreference manager 1704. In some embodiments, summary personalizer 1256may enable a user to interact with PGS 1202 by personalizing a generatedsummary text. In some such embodiments, preference manager 1704 mayutilize these interactions to learn one or more preferences of a user.After the user interaction, the revised summary text may becomepersonalized summary 1204. Embodiments are not limited in this context.

In one or more embodiments, PGS 1202 may detect more than one pattern ininput 1201, resulting in more than one text templates in the summarytext. In one or more such embodiments, the multiple text templates maybe arranged according to a priority level. In some embodiments, thepriority level defined in input 1301 may be used to arrange the multipletext templates. As previously mentioned, the arrangement may initiallybe carried out by document planner 1502. In various embodiments, PGS1202 is not limited to a static ordering schema based on static prioritylevels. In various such embodiments, instead of a static orderingschema, a flexible or dynamic ordering schema may be utilized togenerate personalized summaries with natural-language text.

For instance, each text template may start with an assigned or initialpriority level. In such instances, the assigned priority levels may bedynamically updated based on user preferences. Upgrading a text templatemay cause the priority level associated with the text template to beraised. Similarly, downgrading a text template may cause the prioritylevel associated with the text template to be lowered. In someembodiments, a user may delete any of the generated text. In one or moreembodiments, this information may be fed to document planner 1502 to beused in future summarizations. Accordingly, in various embodiments, PGS1202 may enable a to tailor the generated natural-text summary (e.g.,personalized summary 1204) based on one or more user preferences. In oneor more embodiments, the personalization capability for the ordering oftext templates may be the same or similar to content-based filtering,such as in recommender systems.

FIG. 18 illustrates an embodiment of personalized summary 1204.Personalized summary 1204 may include image 1410 and textual description1850 with a plurality of text templates (e.g., text templates 1330-1A,1330-1B, 1330-2. In the illustrated embodiments, text template 1330-1Amay be placed first in the textual description 1850 of the personalizedsummary 1204 based on priority level 1332-1 being higher than prioritylevels 1332-2, 1332-3. Similarly, text template 1330-2 may be placedsecond based on priority level 1332-2 being higher than priority level1332-3, but lower than priority level 1332-1. Further, context 1608-1may be inserted into text template 1330-1A, context 1608-2 may beinserted into text template 1330-2, and contexts 1608-3A, 1608-3B may beinserted into text template 1330-1B. Embodiments are not limited in thiscontext.

FIGS. 19A-19B illustrates an embodiment of a logic flow 1900. The logicflow 1900 may be representative of some or all of the operationsexecuted by one or more embodiments described herein. More specifically,the logic flow 1900 may illustrate operations performed by processingcircuitry 1220, and/or performed by other component(s) of personalizedgraph summarizer 1202, such as visual pattern detector 1251,personalized pattern creator 1252, summary generator 1254, summarypersonalizer 1256, or context extractor 1258. In one or moreembodiments, these operations may be performed in conjunction withgenerating a personalized summary 1204 or learning a new graph-type orpattern model. Embodiments are not limited in this context.

In the illustrated embodiment shown in FIGS. 19A-19B, the logic flow1900 may begin at block 1902. At block 1902 a data visualizationcomprising a graph image may be identified. For instance, visual patterndetector 1251 may identify a data visualization comprising a graph imagein input 1201. In some embodiments, the data visualization comprisingthe graph image may be an image file received as input 1201. In variousembodiments, identification of the data visualization may be automated.

Continuing to block 1904, a set of graph-type correlation scores with agraph-type correlation score for each graph type of a plurality of graphtypes may be determined. In logic flow 1900, each graph type correlationscore may be based on a comparison of at least a portion of the graphimage with one or more graph-type models associated with each graph typeof the plurality of graph types. For instance, graph-type identifier1402 may include GTCSA 1412 to compare the graph image (e.g., 1410) toeach of graph types 1346-1, 1346-2, 1346-n using graph-type models1372-1, 1372-2, 1372-n, respectively. In such instances, one or more ofgraph-type models 1372-1, 1372-2, 1372-n may include graph-typeclassifier 1366 or another graph-type classifier generated by classifiertrainer 1308. In some embodiments, each graph-type model may take thegraph image as input and output an associated graph-type correlationscore. For example, GTCSA 1412 may provide image 1410 as input tograph-type model 1372-2 to generate graph-type correlation score 1422-2for graph type 1346-2. In such examples, similarly GTCSA 1412 maygenerate graph-type correlation score 1422-1 for graph type 1346-1 usinggraph-type model 1372-1 and graph-type correlation score 1422-n forgraph type 1346-n using graph-type model 1372-n. In one or moreembodiments, determination of the set of graph-type correlation scoresmay be automated.

Proceeding to block 1906, the set of graph-type correlation scores maybe evaluated to identify a graph type of the graph image. In someembodiments, graph-type correlation score evaluator 1424 may comparegraph-type correlation scores 1422-1, 1422-2, 1422-n to determineidentified graph-type 1426. For instance, graph type 1346-2 may beselected as identify graph-type 1426 in response to having the highestgraph-type correlation score. In various embodiments, identification ofthe graph type of the graph image may be automated.

At block 1908 a set of patterns may be retrieved based on the graph typeof the graph image. In logic flow 1900, each pattern in the set ofpatterns may include one or more pattern examples. For instance, ifgraph type 1346-1 is selected as identified graph-type 1426, thenpatterns 1348-1, 1348-2, 1348-n may be retrieved from pattern examplescollection 1344. In such instances, pattern 1348-1 may include patternexamples 1350-1, pattern 1348-2 may include pattern examples 1350-2, andpattern 1348-n may include pattern examples 1350-n. In some embodiments,retrieval of the set of patterns may be automated.

Continuing to block 1910, a set of region of interest correlation scoresmay be determined for the graph image based on matching the one or morepattern examples of each pattern in the set of patterns with at least aportion of the graph image. In logic flow 1900, the set of region ofinterest correlation scores may include at least one region of interestcorrelation score for each pattern in the set of patterns. For instance,ROI confidence score assessor 1428 may determine a confidence score foreach of pattern example(s) 1450-1, 1450-2, 1450-n. In some embodiments,the matching may utilize a sliding window method to compute correlationscores. For example, portions or patches of the example image (e.g.,pattern example 1350-2) may be overlaid on image 1410 in a plurality ofpositions, such as by being slid horizontally and vertically over image1410. In such instances, a correlation score may be computed for eachpatch position (i.e., each of the plurality of positions). In one ormore embodiments, determination of the set of region of interestcorrelation scores may be automated.

Proceeding to block 1912, the set of region of interest correlationscores may be evaluated to identify one or more candidate regions ofinterest comprising in the graph image with each candidate region ofinterest comprising a portion of the graph image. In variousembodiments, ROI confidence score evaluator 1430 may evaluate the set ofregion of interest correlation scores determined by ROI confidence scoreassessor 1428 to identify candidate ROI(s) 1432. In embodiments thatutilize the sliding window method to compute the ROI correlation scores,candidate ROIs 1432 may be identified as patch positions with anassociated correlation score that satisfies one or more criteria. Insome embodiments, the correlation scores may be compared to a thresholdto determine candidate ROIs 1432. In other embodiments, the correlationscores may be compared to each other to determine candidate ROIs 1432.For example, the top five correlation scores for each example image maybe selected as candidate ROIs 1432. In various embodiments,identification of the one or more candidate regions of interest may beautomated.

At block 1914 a set of pattern models may be retrieved based on the setof candidate regions of interest of the graph image. In logic flow 1900each candidate region of interest in the set of candidate regions ofinterest may be associated with one pattern model in the set of patternmodels and each pattern model in the set of pattern models may beassociated with one pattern in the set of patterns. In variousembodiments, the set of pattern models may include the pattern model foreach pattern associated with a candidate region of interest. Forinstance, if candidate ROIs 1432 include one candidate ROI associatedwith pattern 1352-1 and one candidate ROI associated with pattern1352-2, then the set of pattern models retrieved may include patternmodel 1376-1 and pattern model 1376-2. In some embodiments, retrieval ofthe set of pattern models may be automated. At block 1916 the logic flow1900 may proceed to block 1918 in FIG. 19B.

Continuing to block 1918, each candidate region of interest in the setof candidate regions of interest may be compared to an associatedpattern model in the set of pattern models to determine a set of patternmodel correlation scores. In logic flow 1900, the set of pattern modelcorrelation scores may include a pattern model correlation score foreach candidate region of interest of the one or more candidate regionsof interest. For instance, graph-type identifier 1406 may include PMCSA1434 to compare each candidate ROI in the set of candidate ROIs to anassociated pattern model. In such instances, PMCSA 1434 may operate thesame or similar to GTCSA 1412, except with pattern models instead ofgraph-type models. In various embodiments, determining the set ofpattern model correlation scores may be automated.

Proceeding to block 1920, one or more detected patterns may beidentified based on the set of pattern model correlation scores. Forinstance, pattern model correlation score evaluator 1440 may comparepattern model correlation scores 1438-1, 1438-2, 1438-n to identifydetected pattern(s) 1444. In some embodiments, the correlation scoresmay be compared to a threshold to determine detected pattern(s) 1444. Inother embodiments, the correlation scores may be compared to each otherto determine detected pattern(s) 1444. For example, the top fivecorrelation scores may be selected as detected pattern(s) 1444. Invarious embodiments, identification of the one or more candidate regionsof interest may be automated.

At block 1922, one or more text templates may be retrieved based on theone or more detected patterns. In logic flow 1900, the one or more texttemplates may include at least one portion of text associated with eachdetected pattern of the one or more detected patterns, and each texttemplate of the one or more text templates may be associated with apriority level. For instance, if a detected pattern corresponds to input1301, text template 1330 may be retrieved. In such instances, texttemplate 1330 may be associated with priority level 1332. In variousembodiments, retrieval of the one or more text templates may beautomated.

Continuing to block 1924, the one or more text templates may be arrangedin an order based on the priority level associated with each texttemplate to generate a textual description of the graph image. Forinstance, summary generator 1254 may operate to generate textualdescription 1850 of the graph image based on the priority levelsassociated with each text template. In various embodiments, the textualdescription may be personalized based on input from context extractor1258 and/or summary personalizer 1256. In various embodiments,generation of the textual description may be automated. Proceeding toblock 1926, a personalized summary comprising the graph image and thetextual description of the graph image may be produced. For example,personalized summary 1204 may be produced by summary generator 1254 thatincludes image 1410 and textual description 1850 (see e.g., FIG. 18). Invarious embodiments, generation of the textual description may beautomated. In some embodiments, production of the personalized summarymay be automated.

In various embodiments, processing circuitry 1220 may include any of awide variety of commercially available processors. Further, one or moreof these processors may include multiple processors, a multi-threadedprocessor, a multi-core processor (whether the multiple cores coexist onthe same or separate dies), and/or a multi-processor architecture ofsome other variety by which multiple physically separate processors arelinked.

However, in a specific embodiment, the processing circuitry 1220 ofsystem 1205 may be selected to efficiently perform the generations ofpersonalized summary 1204 based on input 1201. Alternatively, oradditionally, the processors of one or more node devices may be selectedto efficiently perform one or more operations described herein. In someembodiments, one or more operations described herein may be performed atleast partially in parallel. By way of example, the processing circuitry1220 or other processors may incorporate a single-instructionmultiple-data (SIMD) architecture, may incorporate multiple processingpipelines, and/or may incorporate the ability to support multiplesimultaneous threads of execution per processing pipeline.

In various embodiments, one or more portions of the processing or logicflows described herein, including the components of which each iscomposed, may be selected to be operative on whatever type of processoror processors that are selected to implement applicable ones of theprocessing circuitry 1220 or other processors utilized by PGS 1202. Invarious embodiments, each of these one or more portions of theprocessing or logic flows described herein may include one or more of anoperating system, device drivers and/or application-level routines(e.g., so-called “software suites” provided on disc media, “applets”obtained from a remote server, etc.). Where an operating system isincluded, the operating system may be any of a variety of availableoperating systems appropriate for processing circuitry 1220 or otherprocessors. Where one or more device drivers are included, those devicedrivers may provide support for any of a variety of other components,whether hardware or software components, described herein.

In various embodiments, each of the storage 1215 and memory 1210 may bebased on any of a wide variety of information storage technologies,including volatile technologies requiring the uninterrupted provision ofelectric power, and/or including technologies entailing the use ofmachine-readable storage media that may or may not be removable. Thus,each of these storages may include any of a wide variety of types (orcombination of types) of storage device, including without limitation,read-only memory (ROM), random-access memory (RAM), dynamic RAM (DRAM),Double-Data-Rate DRAM (DDR-DRAM), synchronous DRAM (SDRAM), static RAM(SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), flash memory, polymermemory (e.g., ferroelectric polymer memory), ovonic memory, phase changeor ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS)memory, magnetic or optical cards, one or more individual ferromagneticdisk drives, non-volatile storage class memory, or a plurality ofstorage devices organized into one or more arrays (e.g., multipleferromagnetic disk drives organized into a Redundant Array ofIndependent Disks array, or RAID array). It should be noted thatalthough each of these storages is depicted as a single block, one ormore of these may include multiple storage devices that may be based ondiffering storage technologies. Thus, for example, one or more of eachof these depicted storages may represent a combination of an opticaldrive or flash memory card reader by which programs and/or data may bestored and conveyed on some form of machine-readable storage media, aferromagnetic disk drive to store programs and/or data locally for arelatively extended period, and one or more volatile solid state memorydevices enabling relatively quick access to programs and/or data (e.g.,SRAM or DRAM). It should also be noted that each of these storages maybe made up of multiple storage components based on identical storagetechnology, but which may be maintained separately as a result ofspecialization in use (e.g., some DRAM devices employed as a mainstorage while other DRAM devices employed as a distinct frame buffer ofa graphics controller). However, in a specific embodiment, the storage1215 of one or more of the node may be implemented with a redundantarray of independent discs (RAID) of a RAID level selected to providefault tolerance to prevent loss of one or more of these datasets and/orto provide increased speed in accessing one or more of these datasets.

In various embodiments, one or more of the interfaces described herein(e.g., interfaces 1225 or user interface 1702) may each be any of avariety of types of input device that may each employ any of a widevariety of input detection and/or reception technologies. Examples ofsuch input devices include, and are not limited to, microphones, remotecontrols, stylus pens, card readers, finger print readers, virtualreality interaction gloves, graphical input tablets, joysticks,keyboards, retina scanners, the touch input components of touch screens,trackballs, environmental sensors, and/or either cameras or cameraarrays to monitor movement of persons to accept commands and/or dataprovided by those persons via gestures and/or facial expressions. Invarious embodiments, each of the displays 1580 and 1780 may each be anyof a variety of types of display device that may each employ any of awide variety of visual presentation technologies. Examples of such adisplay device includes, and is not limited to, a cathode-ray tube(CRT), an electroluminescent (EL) panel, a liquid crystal display (LCD),a gas plasma display, etc. In some embodiments, one or more of theinterfaces may be a touchscreen display.

In various embodiments, interfaces 1225 of PGS 1202 may include one ormore network interfaces that employ any of a wide variety ofcommunications technologies enabling these devices to be coupled toother devices as has been described. Each of these interfaces includescircuitry providing at least some of the requisite functionality toenable such coupling. However, each of these interfaces may also be atleast partially implemented with sequences of instructions executed bycorresponding ones of the processors (e.g., to implement a protocolstack or other features). Where electrically and/or optically conductivecabling is employed, these interfaces may employ timings and/orprotocols conforming to any of a variety of industry standards,including without limitation, RS-232C, RS-422, USB, Ethernet(IEEE-802.3) or IEEE-1394. Where the use of wireless transmissions isentailed, these interfaces may employ timings and/or protocolsconforming to any of a variety of industry standards, including withoutlimitation, IEEE 802.11a, 802.11ad, 802.11ah, 802.11ax, 802.11b,802.11g, 802.16, 802.20 (commonly referred to as “Mobile BroadbandWireless Access”); Bluetooth; ZigBee; or a cellular radiotelephoneservice such as GSM with General Packet Radio Service (GSM/GPRS),CDMA/1xRTT, Enhanced Data Rates for Global Evolution (EDGE), EvolutionData Only/Optimized (EV-DO), Evolution For Data and Voice (EV-DV), HighSpeed Downlink Packet Access (HSDPA), High Speed Uplink Packet Access(HSUPA), 4G LTE, etc. However, in a specific embodiment, a networkinterface of interfaces 1225 may be implemented with multiplecopper-based or fiber-optic based network interface ports to provideredundant and/or parallel pathways in exchanging data.

In various embodiments, the processing and/or storage resources of PGS1202 may be divided among the multiple systems. In various suchembodiments, one or more API architectures may support communicationsamong the multiple systems. The one or more API architectures may beconfigured to and/or selected to conform to any of a variety ofstandards for distributed processing, including without limitation, IEEEP2413, AllJoyn, IoTivity, etc. By way of example, a subset of API and/orother architectural features of one or more of such standards may beemployed to implement the relatively minimal degree of coordinationdescribed herein to provide greater efficiency in parallelizingprocessing of data, while minimizing exchanges of coordinatinginformation that may lead to undesired instances of serialization amongprocesses. However, it should be noted that the parallelization ofstorage, retrieval and/or processing of data among multiple systems isnot dependent on, nor constrained by, existing API architectures and/orsupporting communications protocols. More broadly, there is nothing inthe manner in which the data may be organized in storage, transmissionand/or distribution via network interface of interfaces 1225 that isbound to existing API architectures or protocols.

Some systems may use Hadoop®, an open-source framework for storing andanalyzing big data in a distributed computing environment. Some systemsmay use cloud computing, which can enable ubiquitous, convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, servers, storage, applications and services)that can be rapidly provisioned and released with minimal managementeffort or service provider interaction. Some grid systems may beimplemented as a multi-node Hadoop® cluster, as understood by a personof skill in the art. ApacheTM Hadoop® is an open-source softwareframework for distributed computing.

1. An apparatus comprising a processor and a storage to store instructions that, when executed by the processor, cause the processor to perform operations comprising: identify a data visualization comprising a graph image; determine a set of graph-type correlation scores for the graph image, the set of graph-type correlation scores to include a graph-type correlation score for each graph type of a plurality of graph types, each graph-type correlation score based on a comparison of at least a portion of the graph image with one or more graph-type models associated with each graph type of the plurality of graph types; evaluate the set of graph-type correlation scores to identify a graph type of the graph image; retrieve a set of patterns based on the graph type of the graph image, each pattern in the set of patterns to include one or more pattern examples; determine a set of region of interest correlation scores for the graph image based on matching the one or more pattern examples of each pattern in the set of patterns with at least a portion of the graph image, the set of region of interest correlation scores to include at least one region of interest correlation score for each pattern in the set of patterns; evaluate the set of region of interest correlation scores to identify one or more candidate regions of interest of the graph image, each of the one or more candidate regions of interest to include a portion of the graph image; retrieve a set of pattern models based on the set of candidate regions of interest of the graph image, each candidate region of interest in the set of candidate regions of interest associated with one pattern model in the set of pattern models, and each pattern model in the set of pattern models associated with one pattern in the set of patterns; compare each candidate region of interest in the set of candidate regions of interest to an associated pattern model in the set of pattern models to determine a set of pattern model correlation scores, the set of pattern model correlation scores to include a pattern model correlation score for each candidate region of interest of the one or more candidate regions of interest; identify one or more detected patterns based on the set of pattern model correlation scores; retrieve one or more text templates based on the one or more detected patterns, the one or more text templates to include at least one portion of text associated with each detected pattern of the one or more detected patterns, each text template of the one or more text templates associated with a priority level; arrange the one or more text templates in an order based on the priority level associated with each text template to generate a textual description of the graph image; and generate a personalized summary of the graph image, the summary of the graph image comprising the graph image and the textual description of the graph image.
 2. The apparatus of claim 1, wherein the processor is caused to perform operations comprising: detect a portion of the graph image with contextual information; extract a textual element from the portion of the graph image with contextual information; and insert at least a portion of the textual element extracted from the portion of the graph image with contextual information into at least one text template of the one or more text templates to generate the textual description of the graph image.
 3. The apparatus of claim 1, wherein the processor is caused to perform operations comprising: identify a component of the graph image based on the graph type; detect a portion of the graph image with potential contextual information; and determine contextual information is absent from the portion of the graph image with potential contextual information based on the component of the graph image identified based on the graph type.
 4. The apparatus of claim 1, matching a pattern example of a pattern in the set of patterns with at least a portion of the graph image comprising: overlay at least a portion of the pattern example on the graph image in a plurality of positions; and compute a region of interest correlation score in the set of region of interest correlation scores for each of the plurality of positions.
 5. The apparatus of claim 1, wherein the processor is caused to perform operations comprising: receive an additional pattern example; and update a pattern model in the set of pattern models based on the additional pattern example.
 6. The apparatus of claim 1, each pattern model correlation score to indicate a likelihood of a respective candidate region of interest of the one or more candidate regions of interest including an associated pattern.
 7. The apparatus of claim 1, wherein the processor is caused to perform operations comprising: present the one or more text templates arranged based on the priority level associated with each template sentence via a user interface; arrange the one or more text templates in an updated order based on input received via the user interface; alter a priority level of at least one of the one or more text templates based on the updated order; and generate the textual description of the graph image based on the priority level associated with each text template, the priority level associated with each text template to include the priority level of the at least one of the one or more text templates altered based on the updated order.
 8. The apparatus of claim 1, wherein the processor is caused to perform operations comprising: alter the priority level of a text template based on the input received via a user interface.
 9. The apparatus of claim 1, at least one pattern in the set of patterns comprising a personalized pattern, wherein the processor is caused to perform operations comprising: create the personalized pattern based on one or more example graph images and one or more pattern examples identified in the example graph images based on input received via a user interface.
 10. The apparatus of claim 9, wherein the processor is caused to perform operations comprising: associate one or more of a priority level, a template sentence, or a graph type with the personalized pattern based on input received via the user interface.
 11. A computer-implemented method, comprising: identifying a data visualization comprising a graph image; determining a set of graph-type correlation scores for the graph image, the set of graph-type correlation scores to include a graph-type correlation score for each graph type of a plurality of graph types, each graph-type correlation score based on a comparison of at least a portion of the graph image with one or more graph-type models associated with each graph type of the plurality of graph types; evaluating the set of graph-type correlation scores to identify a graph type of the graph image; retrieving a set of patterns based on the graph type of the graph image, each pattern in the set of patterns to include one or more pattern examples; determining a set of region of interest correlation scores for the graph image based on matching the one or more pattern examples of each pattern in the set of patterns with at least a portion of the graph image, the set of region of interest correlation scores to include at least one region of interest correlation score for each pattern in the set of patterns; evaluating the set of region of interest correlation scores to identify one or more candidate regions of interest of the graph image, each of the one or more candidate regions of interest to include a portion of the graph image; retrieving a set of pattern models based on the set of candidate regions of interest of the graph image, each candidate region of interest in the set of candidate regions of interest associated with one pattern model in the set of pattern models, and each pattern model in the set of pattern models associated with one pattern in the set of patterns; comparing each candidate region of interest in the set of candidate regions of interest to an associated pattern model in the set of pattern models to determine a set of pattern model correlation scores, the set of pattern model correlation scores to include a pattern model correlation score for each candidate region of interest of the one or more candidate regions of interest; identifying one or more detected patterns based on the set of pattern model correlation scores; retrieving one or more text templates based on the one or more detected patterns, the one or more text templates to include at least one portion of text associated with each detected pattern of the one or more detected patterns, each text template of the one or more text templates associated with a priority level; arranging the one or more text templates in an order based on the priority level associated with each text template to generate a textual description of the graph image; and generating a personalized summary of the graph image, the summary of the graph image comprising the graph image and the textual description of the graph image.
 12. The computer-implemented method of claim 11, comprising: detecting a portion of the graph image with contextual information; extracting a textual element from the portion of the graph image with contextual information; and inserting at least a portion of the textual element extracted from the portion of the graph image with contextual information into at least one text template of the one or more text templates to generate the textual description of the graph image.
 13. The computer-implemented method of claim 11, comprising: identifying a component of the graph image based on the graph type; detecting a portion of the graph image with potential contextual information; and determining contextual information is absent from the portion of the graph image with potential contextual information based on the component of the graph image identified based on the graph type.
 14. The computer-implemented method of claim 11, matching a pattern example of a pattern in the set of patterns with at least a portion of the graph image comprising: overlaying at least a portion of the pattern example on the graph image in a plurality of positions; and computing a region of interest correlation score in the set of region of interest correlation scores for each of the plurality of positions.
 15. The computer-implemented method of claim 11, comprising: receiving an additional pattern example; and updating a pattern model in the set of pattern models based on the additional pattern example.
 16. The computer-implemented method of claim 11, each pattern model correlation score to indicate a likelihood of a respective candidate region of interest of the one or more candidate regions of interest including an associated pattern.
 17. The computer-implemented method of claim 11, comprising: presenting the one or more text templates arranged based on the priority level associated with each template sentence via a user interface; arranging the one or more text templates in an updated order based on input received via the user interface; altering a priority level of at least one of the one or more text templates based on the updated order; and generating the textual description of the graph image based on the priority level associated with each text template, the priority level associated with each text template to include the priority level of the at least one of the one or more text templates altered based on the updated order.
 18. The computer-implemented method of claim 11, comprising: altering the priority level of a text template based on the input received via a user interface.
 19. The computer-implemented method of claim 11, wherein at least one pattern in the set of patterns comprising a personalized pattern, and comprising creating the personalized pattern based on one or more example graph images and one or more pattern examples identified in the example graph images based on input received via a user interface.
 20. The computer-implemented method of claim 19, comprising associating one or more of a priority level, a template sentence, or a graph type with the personalized pattern based on input received via the user interface.
 21. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, the computer-program product including instructions operable to cause a processor to perform operations comprising: identify a data visualization comprising a graph image; determine a set of graph-type correlation scores for the graph image, the set of graph-type correlation scores to include a graph-type correlation score for each graph type of a plurality of graph types, each graph-type correlation score based on a comparison of at least a portion of the graph image with one or more graph-type models associated with each graph type of the plurality of graph types; evaluate the set of graph-type correlation scores to identify a graph type of the graph image; retrieve a set of patterns based on the graph type of the graph image, each pattern in the set of patterns to include one or more pattern examples; determine a set of region of interest correlation scores for the graph image based on matching the one or more pattern examples of each pattern in the set of patterns with at least a portion of the graph image, the set of region of interest correlation scores to include at least one region of interest correlation score for each pattern in the set of patterns; evaluate the set of region of interest correlation scores to identify one or more candidate regions of interest of the graph image, each of the one or more candidate regions of interest to include a portion of the graph image; retrieve a set of pattern models based on the set of candidate regions of interest of the graph image, each candidate region of interest in the set of candidate regions of interest associated with one pattern model in the set of pattern models, and each pattern model in the set of pattern models associated with one pattern in the set of patterns; compare each candidate region of interest in the set of candidate regions of interest to an associated pattern model in the set of pattern models to determine a set of pattern model correlation scores, the set of pattern model correlation scores to include a pattern model correlation score for each candidate region of interest of the one or more candidate regions of interest; identify one or more detected patterns based on the set of pattern model correlation scores; retrieve one or more text templates based on the one or more detected patterns, the one or more text templates to include at least one portion of text associated with each detected pattern of the one or more detected patterns, each text template of the one or more text templates associated with a priority level; arrange the one or more text templates in an order based on the priority level associated with each text template to generate a textual description of the graph image; and generate a personalized summary of the graph image, the summary of the graph image comprising the graph image and the textual description of the graph image.
 22. The computer-program product of claim 21, including instructions operable to cause the processor to perform operations comprising: detect a portion of the graph image with contextual information; extract a textual element from the portion of the graph image with contextual information; and insert at least a portion of the textual element extracted from the portion of the graph image with contextual information into at least one text template of the one or more text templates to generate the textual description of the graph image.
 23. The computer-program product of claim 21, including instructions operable to cause the processor to perform operations comprising: identify a component of the graph image based on the graph type; detect a portion of the graph image with potential contextual information; and determine contextual information is absent from the portion of the graph image with potential contextual information based on the component of the graph image identified based on the graph type.
 24. The computer-program product of claim 21, wherein to match a pattern example of a pattern in the set of patterns with at least a portion of the graph image the computer-program product includes instructions operable to cause the processor to perform operations comprising: overlay at least a portion of the pattern example on the graph image in a plurality of positions; and compute a region of interest correlation score in the set of region of interest correlation scores for each of the plurality of positions.
 25. The computer-program product of claim 21, including instructions operable to cause the processor to perform operations comprising: receive an additional pattern example; and update a pattern model in the set of pattern models based on the additional pattern example.
 26. The computer-program product of claim 21, each pattern model correlation score to indicate a likelihood of a respective candidate region of interest of the one or more candidate regions of interest including an associated pattern.
 27. The computer-program product of claim 21, including instructions operable to cause the processor to perform operations comprising: present the one or more text templates arranged based on the priority level associated with each template sentence via a user interface; arrange the one or more text templates in an updated order based on input received via the user interface; alter a priority level of at least one of the one or more text templates based on the updated order; and generate the textual description of the graph image based on the priority level associated with each text template, the priority level associated with each text template to include the priority level of the at least one of the one or more text templates altered based on the updated order.
 28. The computer-program product of claim 21, including instructions operable to cause the processor to perform operations comprising: alter the priority level of a text template based on the input received via a user interface.
 29. The computer-program product of claim 21, at least one pattern in the set of patterns comprising a personalized pattern, and the computer-program product including instructions operable to cause the processor to perform operations comprising: create the personalized pattern based on one or more example graph images and one or more pattern examples identified in the example graph images based on input received via a user interface.
 30. The computer-program product of claim 29, including instructions operable to cause the processor to perform operations comprising: associate one or more of a priority level, a template sentence, or a graph type with the personalized pattern based on input received via the user interface. 